Trending February 2024 # Tourist Places In Kasauli (Updated 2023) # Suggested March 2024 # Top 6 Popular

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Kasauli Town

Kasauli is a small hill town located 1927 meters above sea level In Himachal Pradesh. Kasauli also functions as an Indian military base. Kasauli is a popular destination as its beautiful snow-covered mountains, extensive woods, and hibernal climate attract tourists. The Kasauli is a British cantonment town founded in 1842.

Tourist Places in Kasauli

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#1 Monkey Point

The Monkey point is one of the most popular tourist attractions and the highest point in Kasauli hill town. This tourist spot is easy to commute to, which is only 4 km from the bus stop. Monkey Point has an amazing temple to Lord Hanuman that is constantly populated with monkeys. Nearby cities of Kalka, Chandigarh, Panchkula, and the Sutlej River a nearby distance from monkey point. Visitors might also have a distant view of the snow-covered peak of “Choor Chandni,” the highest mountain in the Lower Himalayan range, on a clear day.

According to local beliefs, Lord Hanuman’s foot touched this location while bringing the Sanjeevani herb for Laxman. Another fact that might leave the visitor in surprise is that the temple bears the Lord’s footprints since the shape of the hill is like a foot. This odd rock shape is popular as “Tapp’s Nose.”

#2 Mall Road #3 Gilbert Trail

An easy hike on Gilbert Trail is a distance of 1.5km. Gilbert Trail is the walk leaves from Kasauli Club and ends close to Sunset Point at the Air Force Station. At the beginning of the trail, the path is broad, but it gradually becomes narrower. Gilbert Tail is notable for its visitors and different species of birds. The Gilbert trail is 2 km away from Kasauli. Gilbert trail is one of the amazing tourist spots situated near the sunset point of Kasauli.

#4 Sunset Point and Lovers Lane

A beautiful spot but a remarkably calm soul soothing location to take in the sunset and the surrounding pine-covered valleys and scenery at sunset point. Lovers Lane is a trail or course that offers a calmer stroll through the valleys and a portion of Kasauli’s most quiet and radiant landscape, is proximally near Nightfall Point. It is best to depart the area before sunset or after midnight because it is typically an isolated area with limited conveniences.

#5 Timber Trail

The exhilarating ropeway rides at this location are its most well-known draw. A refreshing pleasure is sliding through the mountain paths while surrounded by breathtaking natural beauty. Without even moving your feet, the deep gorges directly beneath the ropeway will give you an unbelievable rush of excitement! There are also a few hotels that offer the ideal lodging.

#6 Christ (Anglican) Church

This church is a well-liked tourist destination because it is present in the city’s center and has a busy Mall road surrounding it. The construction of the church is in the year 1853 and exudes the majesty of the Gothic architectural style. This church is a stunning building to look at and features lovely Italian and Spanish stained glass windows showing Christ, Mother, Saint Barnabas, and Saint Francis. All visitors that come to the country should visit this serene location.

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10 Best Digital Marketing Agencies In Karnataka 2023 Updated

Digital marketing is the process of using electronic devices to promote and sell products or services. This can be done through the use of digital channels such as the Internet, social media, email, and mobile phones. Digital marketing has become an important part of modern marketing because it allows businesses to reach a larger audience more easily and at a lower cost than traditional marketing methods.

How to Choose a Digital Marketing Agency?

When choosing digital marketing agencies in Karnataka, it is important to consider the size and scope of the agency, as well as its areas of expertise. The agency should also have a good understanding of your business and your target market. Furthermore, the agency should be able to provide a detailed proposal outlining the services they can provide and how these services will benefit your business.

Types of Digital Marketing Services

Search Engine Optimization (SEO)

Search Engine Marketing (SEM)

Content Marketing

Social Media Marketing (SMM)

Affiliate Marketing

Email Marketing

Mobile Marketing

Role of a Digital Marketing Agency

A digital marketing agency plays a vital role in the success of a business in the modern world. They are responsible for creating and executing marketing campaigns that promote a company’s products or services online. Without a digital marketing agency, it would be very difficult for a business to reach its target market and achieve success.

There are many different types of digital marketing agencies, each with unique skills and specialities. Some agencies specialize in search engine optimization (SEO), while others may focus on social media marketing or email marketing. It is important to choose an agency with experience and expertise in the specific type of marketing that you need.

If you are sceptical about hiring an agency, Upskill with the best digital marketing courses in Bangalore, Karnataka, and start marketing for your company from scratch.

Attending a digital marketing course might also help you assess possible digital marketing companies more effectively. You may ask more educated inquiries and assess the agency’s team’s competency if you have a deeper grasp of digital marketing strategies and approaches.

An excellent digital marketing agency will work closely with you to understand your business goals and objectives.

If you are looking for digital marketing services in Karnataka, EchoVME is your right partner. The team of experienced professionals can help you with all your online marketing needs. Contact today to get started!

Here are the top 10 digital marketing agencies in Karnataka for your business. Let’s get started!

Disclaimer: All the digital marketing agencies and the information listed were taken from various sources on the Internet and can be changed at the agency’s convenience. We do not take any responsibility if the information mismatches. So please cross-check the information from their website once before you take any further action.

PS: If you wish to hire digital marketers specializing in different skills for your business, you may turn to the specialists at Digital Scholar.

1. echoVME Digital 

echovme Digital is a top digital marketing agency in Karnataka, India. They offer high-quality services to clients all over the world. With a team of experienced and skilled professionals, they have been delivering great results for clients in the rapidly growing digital marketing industry.

Agency employs skilled creative minds who are constantly innovating and finding new ways to elevate their brands digitally. Team members have a set of skills and expertise that allow them to excel in this ever-changing field.

The agency offers a variety of digital marketing services to companies looking to improve their online presence. Additionally, they offer online digital marketing courses through their educational extension, Digital Scholar. Digital Scholar, which was established in 2023, is an excellent resource for those wanting to expand their digital marketing skills.

There’s no doubt that echoVME is one of the best digital marketing agencies in Karnataka. They have the expertise and experience to help you with your online marketing needs, whether you’re looking to improve your strategy or learn more about digital marketing.

Services Offered

Social media marketing

Influencer marketing 

Search engine optimization

Website development 

Branding strategy 

Performance marketing 

Video production 

Event marketing 

Instagram marketing 

Digital consultancy 

Industries Served

 Beauty and Salon





 IT – Tech – SAAS



 Real estate


Clients Google Reviews & Ratings of echoVME Digital- Digital Marketing Agency

echoVME DIGITAL has over 140 Google reviews. echoVME ratings as per Google Business Profile is 4.1/5, and Ambition Box rating is 4.8/5.

Below are the contact details for the digital marketing agency – echoVME Digital, by contacting or mailing them (they will get back to you at the earliest between working hours)

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2. Digital Corsel Services Offered

Website development

Content writing 


Search engine optimization

Search engine marketing 

Social media marketing 

Address: #692, 5th floor A, Kay Arr Ivy building, 6th A cross, 16th Main Rd, 3rd Block, Koramangala, Bengaluru, Karnataka 560034

Phone: 08019997777

3. BLeap Digital

BLeap Digital is one of the best digital marketing agencies in Karnataka. They provide a full range of services including SEO, PPC, Social Media, and more. They are dedicated to helping clients expand their online presence and reach their goals.

Services Offered

Social media marketing

Content marketing 

Search engine optimization

Search engine marketing 

Website development 

Address: Flat 806, GK Meadows, Shikari Palya Main Road, Electronic City Phase -1 Bangalore Karnataka 560105, Shikaripalya Main Road, Electronic City, Bengaluru, Karnataka 560100

Phone: 09382809420

4. RDS Digital

RDS Digital is a digital marketing agency in Karnataka that has been providing quality digital marketing services for years. They have a team of highly skilled and experienced digital marketing experts who are always ready to help our clients grow their businesses online. They offer a wide range of digital marketing services such as SEO, SEM, social media marketing, email marketing, and more. They also offer a wide range of web design and development services. They are always ready to help clients grow their businesses online.

Services Offered

Content writing 

Search engine optimization

Search engine marketing 

Social media marketing 

Website development


Address: #80, 3rd Floor, 1st Main Rd, Opp. Big Bazaar, Santrupthi Nagar, JP Nagar 7th Phase, Bangalore-78, Karnataka 560078

Phone: 06363738608

5. First Launch

First Launch is one of the leading digital marketing agencies in Karnataka. They offer a wide range of services that include SEO, social media marketing, web design and development, and more. They are a team of experienced professionals who are passionate about helping our clients grow their businesses online.  They believe in providing quality services at affordable prices.

Services Offered

Website development

Content writing 


Search engine optimization

Search engine marketing 

Social media marketing 

Address: No.922, 1st Floor, AVR Arcade, 28th Main Rd, Jayanagara 9th Block, Bengaluru, Karnataka 560069

Phone: 097314 74769

Services Offered

Social media marketing

Content marketing 

Search engine optimization

Search engine marketing 

Website development 

Address: 5490, Second Floor, High Tension Double Rd, Vijay Nagar 2nd Stage, Mysuru, Karnataka 570017 

7. Digikraft 

Digikraft is one of the best digital marketing agencies in Karnataka. They are experts in SEO, social media marketing, and email marketing. They have helped numerous businesses grow their online presence and reach their target audiences. If you are looking for a digital marketing agency that can help you take your business to the next level, then you should definitely consider working with Digikraft.

Services Offered

Content writing 

Search engine optimization

Search engine marketing 

Social media marketing 

Website development


Address: #936, 20th Main Rd, JP Nagar 2nd Stage, JP Nagar, Mysuru, Karnataka 570008 

8. Digiconiq  Services Offered

Website development

Content writing 


Search engine optimization

Search engine marketing 

Social media marketing 

Address: 304, 8th Cross Rd, Kumbarakoppal, Mahadeshwara Badavane Layout, Mysuru, Karnataka 570016

9. Theorem

Theorem is a digital marketing agency in Karnataka. With years of experience and a team of highly skilled professionals, they have helped many businesses grow their online presence and reach their target audiences. If you’re looking for an agency that can help you take your business to the next level, look no further than Theorem.

Services Offered

Social media marketing

Content marketing 

Search engine optimization

Search engine marketing 

Website development 

Address: 67/C, Hootagalli Industrial Area, Mysuru, Karnataka 570018

10. Global Buzz  Services Offered

Content writing 

Search engine optimization

Search engine marketing 

Social media marketing 

Website development


Address: Global Buzz, #4926, 2nd Floor, near Kautilya School, J Block, 3rd stage, Kanakadasa Nagar, Dattgalli, Mysuru, Karnataka 570022


These are the top 10 digital marketing agencies in Karnataka!

Hope the above blog gave you an idea of the top digital marketing agencies in Karnataka. If there are any suggestions or queries, please feel free to reach out to us. 

If you feel we have missed a digital marketing agency that you want to see on the list, then you can reach out to us at [email protected]

And thanks for reading!

Top 10 Business Finance Books (Updated 2023)

Best Books to Learn Business Finance

Business finance books provide strategies for finding funding and making money. These books intend to provide capital for projects, boost earnings, and aid in the long-term financial performance of organizations.

The list of business finance books aids readers at all levels of expertise in their understanding of Business Finance. Beginners can use this as a starting point, and professionals with more expertise can use it as a reference guide. These deal with funding, debt management, profit, return on investment, trade, and valuation.

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Let us discuss each business finance book in detail, outlining their reviews and key points.

Book #1: Valuation: Measuring and Managing the Value of Companies

Buy this book here.


The book explains how to appropriately assess a company while considering variables like taxes, inflation, running and non-operating expenditures, and retirement. It thoroughly explains how to determine a company’s value and get the best returns on investment. It also describes situations and exceptions, like emerging markets and fast-growing businesses.

Key Points:

How to create value, predict and assess performance, and strategic investment & management

A description of the valuation complexities, tactics, and procedures

Information on both fundamental and cutting-edge management practices.

Book #2: The Alchemy of Finance

Author: George Soros

Buy this book here.


The Alchemy of Finance is the written wisdom of renowned investor George Soros. The book offers a framework for financial decision-making and explains Soros’s trading and investment methods. While later chapters predict future trends, earlier chapters examine economics via a historical lens. Although the book’s economic experiment occurred in the 1980s, the lessons learned, and market insights still apply today.

Key Points:

The book discusses economics and reflexivity, the human uncertainty principle, and power relationships.

The multi-billionaire investor offers a new perspective on economic behavior by outlining how the actions of financial actors impact market activity.

It contains unique investment techniques, including the “theory of reflexivity,” which forms their basis.

Book #3: The Personal MBA: Master the Art of Business

Author: Josh Kaufman

Buy this book here.


This book is one of the best books on business finance for beginners. The main point made by the author is that becoming a business expert can be achieved without going to business school. It discusses a variety of MBA programs and has a sizable section on finance that provides a thorough overview of the best ways to make money. It also explains market analysis.

Before launching a new business, one should conduct research, especially if one intends to invest all of their assets. The book serves as a crash course in company operations and is a guide to making businesses profitable and effective.

Key Points:

It explains the Iron Law of the Market, which discusses why firms work according to the size and caliber of the market it services

The book also teaches complementary subjects such as valuation, sales, and improving systems.

It also states how and why adding value for your clients is necessary through the 12 Standard Forms of Value.

It explains cash flow, income statements, purchasing power, lifetime value, pricing, and funding.

Book #4: Profit First: Transform Your Business from a Cash-Eating Monster to a Money-Making Machine

Author: Mike Michalowicz

Buy this book here.

Review: Key Points:

It lays out the realistic actions business owners can take to improve their bank balances.

These summaries suggest a new strategy that ensures ongoing profitability

It guides how to expand businesses and start making money by creating a routine for handling finances.

Book #5: How Finance Works: The HBR Guide to Thinking Smart About the Numbers

Buy this book here.


According to Desai, to achieve professionally, a person must make sensible financial decisions and develop the confidence to defend those decisions to others. The book serves as a primer for the field of financial systems. The manual examines how wise leaders make financial decisions and touches on capital allocation. It simplifies complex ideas into brief explanations for beginners’ easy understanding. However, experienced experts can benefit from it.

Key Points:

The book covers essential concepts such as valuation, financial analysis, capital markets, etc.

The book demystifies finance while guiding readers through its intricate but inexhaustibly fascinating realm.

It covers concepts that teach the skills to comprehend finance functions through exciting case studies, interactive exercises, full-color images, etc.

Book #6: The Intelligent Investor

Author: Benjamin Graham

Buy this book here.

Review: Key Points:

The book guides through various stock market policies like portfolio, dividend, EPS, security analysis, etc.

It uses case studies to explore the historical background of the stock market.

It lays out formulas for reducing risk and achieving better returns.

Book #7: Accounting for the Numberphobic: A Survival Guide for Small Business Owners

Author: Dawn Fotopulos

Buy this book here.


Accounting for the Numberphobic is primarily for business owners with no accounting knowledge. This manual teaches business owners to overcome their fear of doing the math and interpreting the data.

Key Points:

It illustrates using financial statements, ledgers, and profit and loss statements.

Increasing your profits is based on your Net Income Statement

Determine when your company is self-sustaining and how to measure and improve cash flow

It also explores the connection between the balance sheet and the value of a business.

Book #8: How to Read a Financial Report: Wringing Vital Signs Out of the Numbers

Author: Tage C. Tracy and John A. Tracy

Buy this book here.


The book focuses on the information that financial accounts and reports provide about a company. It clarifies sometimes confusing areas of financial reporting and discusses how organizations can “financially design” operating performance. Ultimately, it provides examples and exhibits to help readers understand financial statements and their use.

Key Points:

It provides thorough, detailed instructions for assessing financial information and reading and understanding financial reports.

The book illustrates every aspect of financial reports like income, taxes, cashflows, and others.

It contains several examples and explanations of various financial reporting and analytical tools.

Book #9:The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns

Author: John C. Bogle

Buy this book here.

Review: Key Points:

This guide explores the nuances of index funds and shows readers how to build a strong and stable portfolio that accumulates wealth.

It shows how to create a broadly diversified, inexpensive portfolio without taking risks for picking particular stocks, managers, or switching sectors.

The book tells of setting realistic expectations for stock returns. It is essential to recognize that stock returns come from three factors: dividend yield, income growth, and changes in market valuation.

Book #10: Warren Buffett and the Interpretation of Financial Statements: The Search for the Company with a Durable Competitive Advantage

Author: Mary Buffett and David Clark

Buy this book here.


The book thoroughly examines the writer’s investment philosophies and tactics. It highlights Buffett’s knowledge of maximum business debt, the impact of R&D on profits, etc. It also explains the profitable use of financial statements to make good investment decisions.

Key Points:

The guide offers a step-by-step breakdown of examining investment statements and deciding on a company’s worth.

It explores Buffett’s tried-and-true dos and don’ts by reading an income statement and balance sheet.

The book additionally discloses how much debt a company can hold before it becomes too risky to touch.

It also includes Buffett’s profitability statements and calculations to recognize the business with a resilient, competitive edge.

Wondershare Filmora Video Editor Review (Updated 2023)

Filmora Video Editor

Thomas Boldt

Effectiveness: Lots of features found in professional-level programs

Price: Affordable at $49.99/year or $79.99 lifetime

Ease of Use: Excellent interface that makes complex tasks simple

Support: Not enough tech support documentation

It is a simple yet powerful video editor available for Mac and PC, aimed at the enthusiast and prosumer markets. It’s perfect for a range of basic uses, from creating tutorial videos to editing action camera footage to making viral videos for social media sites.

You probably wouldn’t want to use it to edit a feature-length movie, but for short video work it’s surprisingly effective for its price point, with a good blend of features that are easy to use.

The program has been around for quite a while, reaching version 11 in the latest release. It was originally released as Wondershare Video Editor, but after version 5.1.1 it was rebranded as Filmora. This extensive history has allowed Wondershare to iron out almost all bugs and user experience issues, although some of the newer features need a bit more work before they’re fully reliable.

The program is absolutely safe to use, and both the installer file and the program’s executable file pass virus and malware scan from Microsoft Security Essentials and Malwarebytes AntiMalware. The Mac version also passed scans from Drive Genius.

The installer program available from the official website connects directly to their servers to ensure that you’re downloading the latest and most stable copy of the software currently available. The installation process is simple and straightforward, and it doesn’t attempt to install any unwanted adware, add-ons or other third-party software.

Filmora is not free software, but offers a full-featured free trial with only one usage restriction: exported videos are watermarked with a Filmora banner across the bottom third of the output.

There are two main purchasing options: a one-year license that must be renewed annually for $49.99, or a lifetime license for a single payment of $79.99. These licenses are only valid for a single computer, but multi-seat licenses are also available on a sliding scale depending on the number of copies you want to use simultaneously.

Removing the watermark on exported videos is extremely easy, and only requires that you purchase a license key for the software. There are several ways to do this from within the application, including the prominent red “Register” menu item in the toolbar as well as the “Unregistered” link in the bottom right corner.

Once your registration is complete, you simply enter your license code, and the watermark will be removed on any videos you export in the future.

Why Trust Me for This Filmora Review

My name is Thomas Boldt. I’m a college-educated graphic designer with experience in motion graphic design as well as a dedicated photography instructor, both of which require me to work with video editing software. Creating tutorial videos is one of the best ways to showcase more complicated photography techniques, and high-quality video editing is an essential element for making the learning process as smooth as possible.

I also have extensive experience working with all types of PC software from small open-source programs to industry-standard software suites, so I can easily recognize a well-designed, high-quality program. I’ve put Wondershare Filmora through several tests designed to explore its range of video editing and exporting features and documented all of the results of the process with screenshots you’ll see throughout this review.

I have not received any kind of compensation or consideration from Wondershare to write this Filmora review, and they have no editorial or content input of any kind.

I’ve also contacted the Wondershare support team to test their responsiveness to bug reports and other technical issues, as you can see below from the open ticket I submitted after a problem I experienced during the review process.

Detailed Review of Filmora

The software has a huge range of features, and since we don’t have room to talk about all of them we’re going to focus on the main points that make it worth your time – as well as point out a few issues that might get in your way.

The screenshots I used for this article were taken from the Windows version, but JP was testing the Mac version at the same time and included some comparison screenshots to show the differences in the user interface. He’ll also highlight any feature differences between the two platforms.

Editing Interface

The simplicity of its user interface is one of its most appealing features. The main section you’ll be working with is the timeline, which fills the bottom half of the screen and lets you manage all the different video clips, images, overlays and audio that will become your movie. It’s a simple drag and drop interface that allows you to quickly arrange, trim and edit your various media elements, and it makes composing your video a breeze.

Finally, it’s impossible to rename your tracks, which can make it a bit confusing to find which item you want to edit among a range of similar media elements. It’s not a problem when you’re working on a simple video like the one I made for this Filmora review, but on a larger project, it would be all too easy to get lost in the timeline.

Media Importing

Searching the official website and even some careful Google sleuthing didn’t provide any solutions to this problem, so the only option, in this case, is to send a support ticket to the company and wait for a reply. They replied to me after about 12 hours, but they simply requested that I update to the latest version (which I was already using), and to send them the log files and an accompanying screenshot.

Unfortunately, it seems that this bug isn’t limited to the PC version of Filmora, as JP ran into a similar issue on his Macbook. He could connect to Facebook inside the app, but while it retrieved a list of his photos, it couldn’t retrieve the associated thumbnail images. This makes it more or less impossible to find the right images and videos to import into Filmora, or at the very least time-consuming and frustrating. Clearly, this feature needs a little bit more work before it’s a reliable part of the software.

Screen Recording

Video Effect Presets

If you’re not satisfied with the presets that are included with the software, you can visit the Filmora Effects Store directly from the program to find some new presets that are more to your liking.

This is a useful feature, but while they do occasionally offer some free preset packs, the paid packs are actually quite expensive – some as much as $30, which is a bit much for a program that only costs $60 originally.

Encoding and Exporting

There are a lot of different ways of encoding digital video, and Filmora can encode your videos in almost all of them. The encoding format, bit rate, resolution and audio formats can all be customized to meet your requirements, and you get a handy estimate of the final file size so that you’re not surprised when the encoding process is done. Some social media sites limit the file size of uploaded videos, so this will save you from spending hours encoding a 4K video that turns out to be over the limit.

The export process is easy to use and relatively fast, despite the fact that my graphics card was not supported by the program which prevented me from using the optional GPU acceleration feature (Source: Wondershare support). Most of the supported cards are several years old now, but if you have a computer new enough to include an unsupported card, it’s probably fast enough to handle video encoding quickly without needing to rely on help from the GPU.

One of the more useful export features of Filmora is the ability to export videos directly to Youtube, Vimeo, and Facebook, which is another great productivity booster for aspiring viral video stars. You also have the ability to burn DVDs directly from the program, although there is no support for Blu-Ray discs despite the fact that the program is perfectly capable of outputting HD and 4K videos, neither of which are compatible with DVDs.

Extra Editing Modes

For those of you looking for a more streamlined editing process, Filmora has a couple of extra modes that you can select when the program starts: Easy Mode, Instant Cutter, and Action Cam Tool. These are all designed to do specific jobs, and they are all quite easy to use.

Easy mode, as you might expect, is an extremely streamlined video creator intended for making animated slide shows or quickly combining several clips while automatically adding music, overlays and transitions between clips. Unfortunately, it’s almost a pointless addon because the main program itself is incredibly easy to use. Easy mode will do all the work for you, but it will almost certainly mangle your media along the way, so it’s better to just work in Full Feature Mode.

Instant Cutter and the Action Cam Tool are far more useful, but they really should be integrated into the main program instead of acting as standalone programs. They allow you to manipulate and merge individual video clips with customized speed settings, freeze frames, and image stabilization. They’re great features, but there is no good reason for their functionality not to be integrated into Full Feature Mode where you’ll do the majority of your editing, and switching back and forth between them can be time-consuming and frustrating.

Reasons Behind My Ratings

Effectiveness: 4/5

Filmora does a great job of editing videos at the enthusiast and prosumer level, and despite some issues with its non-essential features such as media importing, GPU acceleration and disc burning, it’s quite effective at its primary tasks. For most users looking for a video editing program, Filmora will handle anything you can throw at it with ease, streamline your creation process and look good while doing it.

Price: 4/5

It is fairly competitively priced, but in order to get the most out of the program, you’re probably going to want to buy some of the add-on effects packs. These are a lot less reasonably priced, with some packs costing as much as $30 – half the price of the program itself. There are other video editors on the market that cost slightly more but provide a little more value for your dollar.

Ease of Use: 5/5

Ease of use is where this editing program really shines. Few video editing programs do such a good job of combining a rich feature set with a simple interface that doesn’t require an extensive training process. Within a few minutes of downloading and installing the program, you can be well on your way to making your first movie, especially if you’re already familiar with other video editing programs. Even if you’re not, the basics are easy to learn, and the Wondershare website has some great introductory training material.

Support: 3/5

Wondershare has been around for a long time, which makes the lack of support information available on their website a bit surprising. They have some good tutorials available on how to use the more basic features of the program, but there are no support forums for users to help each other, and the FAQ section of the site doesn’t provide very many answers. Confusingly, some of the support links within the program itself point to previous versions of the software, which can make it difficult to get the right answers to your questions.

If you find yourself in that spot, like I did when trying to set up social media importing, your only solution is to open a support ticket with the developers and wait for them to get back to you. I don’t know how much of a backlog they have in their support queue, but you may be waiting a while for a reply.

Filmora Alternatives

Camtasia is a very similar program to Filmora, but far more expensive. The primary difference in terms of features is that Camtasia doesn’t rely on presets to create most of its video effects, and instead allows you to create your own animations and presets without requiring a secondary effects program. We also reviewed Camtasia here.

Adobe Premiere Elements is the slightly less-powerful cousin of Adobe’s flagship video editor, but that makes it a better competitor to Filmora. A digital download of the software is available for both Windows and macOS, and while it’s not quite as easy to use as Filmora, it’s also quite a bit more powerful and feature-packed. You can learn more from our Premiere Elements review.

PowerDirector is priced competitively and includes a much larger range of effects that can be used in your videos. It’s also the first video editing program to support 360-degree VR videos, so if you’re looking to specialize in VR content this is a better choice than Filmora. That power comes at the cost of user experience, which means the learning curve is much steeper. We also have a detailed review of PowerDirector here.

If you’re looking for alternatives to the Mac version of Filmora, there’s always Apple’s iMovie app. It’s quite easy to use, it’s free and it has been in development for even longer than Filmora, so it’s worth a look. However, so check your macOS version before installing it.


Filmora is a powerful video editing program that is perfect for users who want to focus on their creativity instead of getting stuck on the technical side of video production. Its careful balance of an easy-to-use interface and professional features make it a good value for beginner and intermediate content creators, but more experienced users will want a solution that offers a bit more control and customization in the editing process.

So, do you find this Filmora review helpful? Share your thoughts below.

Top 10 Ios Interview Questions And Answers {Updated For 2023}

Introduction to iOS Interview Questions and Answers

The following article provides an outline for iOS Interview Questions. iOS is one of the most popular mobile operating systems, mainly developed for Apple iPhone users. Apple Inc. designed and developed it. The iPad and iPod Touch were later added to the list of supported Apple iOS devices after it was first launched in 2007 for the iPhone. Every year, iTunes and over-the-air deliver new major versions of the iOS operating system.

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Below are the 10 crucial 2023 iOS Interview Questions and Answers frequently asked in an interview.

Part 1 – iOS Interview Questions (Basic)

This first part covers basic Interview Questions and Answers.

Q1. Explain the SpringBoard functionality for iOS.


SpringBoard mainly manages the iOS home screen and also launches and bootstraps applications and sets up startup functionalities.

Q2. Explain a few new features of iOS 11.


These are the most common iOS Interview Questions asked in an interview.

The new features of iOS 11 are mentioned below:

IOS 11, users can send money to their friends using the Apple Pay feature.

In iOS 11, a new App Drawer feature has been introduced by Apple. With the help of this newly redesigned App Drawer feature, users will now be easily able to share the contents through the Message App.

In iOS 11, Apple has introduced an updated version of Siri with substantial improvements to its previous versions. The new version of Siri will now offer multiple search results and multiple languages translation support in real-time.

The newly redesigned control center in iOS 11 will now offer the 3D Touch support functionality.

In iOS11, users can sync the messaging app across different iOS devices. So, if the user deletes a message through iPhone, the same will also be deleted from the synced iPad and Mac and vice-versa.

Q3. Explain the vieDidLoad and viewDidAppear.


In the case of viewDidLoad, this is mainly called when the hierarchy loading process for the view controller has been completed and the records have been loaded into memory successfully. Developers can override this method to process the extra initialization on views implemented from nib files.

In the case of viewDidAppear, this is mainly called when the animation process, which is involved in the initial view appearance, has been completed. Developers can override this method to implement tasks that do not interfere with the ongoing animation view process. So, when there is a rapid change in data or view of the animation process, viewDidAppear is an excellent method to implement the system functionalities.

Q4. Explain Completion Handler and Blocks from an iOS perspective.


Blocks are a language-level feature implemented along with C and C++ languages. With regard to the methods or functions, these produce distinctive code portions that can be passed. These can be processed later depending on the system’s capabilities and are not limited to real-time executions. It also implements the codes and displays the same in a much cleaner representative way.

Q5. Explain StoryBoard and Programmatic UIView from an iOS perspective.


Developers can construct a view and add new views to it programmatically for StoryBoard. They primarily use StoryBoard for quick UI representation based on appropriate design specifications. It also produces the end-to-end visualization of the entire system process. But in the case of a duplicate handling process, StoryBoard implementation becomes challenging to update as per the system functionality.

In the case of Programmatic UIView, it generally possesses a bit longer and more tedious approach than StoryBoard. Here it gives a steadier response to the current code and the corresponding views based on the system functionality. It also develops a branching approach to the fundamental principles present and thus becomes more easily unit testable.

Part 2 – iOS Interview Questions (Advanced) Q6. What are the different types of iOS states?


The different types of iOS application states are:

Idle/Not Running State: In this step, the App should remain idle or not running.

Active State: In this step, the App should stay moving and should work as per the user inputs and system functionality.

In-Active State: This step requires the App to remain inactive, which means that it can be executed in the background, but it should not perform any UI-based functionality.

Suspended State: In this state, the App should remain in balanced mode. Here, the app should not execute any code, and its ongoing execution should remain in exact stop mode.

Q7. Which property returns initialized objects?


Q8. Explain App Id and Bundle ID from an iOS perspective.


These are some of the more difficult iOS interview questions. The string “App Id” has two parts. In this situation, the participating development team mostly uses it to track the progress of one or more applications. They use the Bundle Id to identify the particular program in question.

Q9. Explain reuseIdentifier.


This identifier sorts group parameters to implement identical rows in the UITableView artifacts.



Easy development concerning UI standardization.

Very smooth operating system, and thus, the quality usually is higher than Android.

The size of the apps is generally on the higher side than that of Android OS.

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12 Important Model Evaluation Metrics For Machine Learning Everyone Should Know (Updated 2023)


The idea of building machine learning models or artificial intelligence or deep learning models works on a constructive feedback principle. You build a model, get feedback from metrics, make improvements, and continue until you achieve a desirable classification accuracy. Evaluation metrics explain the performance of the model. An important aspect of evaluation metrics is their capability to discriminate among model results.

In this tutorial, you will learn about several evaluation metrics in machine learning, like confusion matrix, cross-validation, AUC-ROC curve, and many more classification metrics.

You will also learn about the different metrics used for logistic regression for different problems.

Lastly, you will learn about cross-validation.

What Are Evaluation Metrics?

Evaluation metrics are quantitative measures used to assess the performance and effectiveness of a statistical or machine learning model. These metrics provide insights into how well the model is performing and help in comparing different models or algorithms.

When evaluating a machine learning model, it is crucial to assess its predictive ability, generalization capability, and overall quality. Evaluation metrics provide objective criteria to measure these aspects. The choice of evaluation metrics depends on the specific problem domain, the type of data, and the desired outcome.

I have seen plenty of analysts and aspiring data scientists not even bothering to check how robust their model is. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach. The ground truth is building a predictive model is not your motive. It’s about creating and selecting a model which gives a high accuracy_score on out-of-sample data. Hence, it is crucial to check the accuracy of your model prior to computing predicted values.

In our industry, we consider different kinds of metrics to evaluate our ml models. The choice of evaluation metric completely depends on the type of model and the implementation plan of the model. After you are finished building your model, these 12 metrics will help you in evaluating your model’s accuracy. Considering the rising popularity and importance of cross-validation, I’ve also mentioned its principles in this article.

Types of Predictive Models

When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). The evaluation metrics used in each of these models are different.

In classification problems, we use two types of algorithms (dependent on the kind of output it creates):

Class output: Algorithms like SVM and KNN create a class output. For instance, in a binary classification problem, the outputs will be either 0 or 1. However, today we have algorithms that can convert these class outputs to probability. But these algorithms are not well accepted by the statistics community.

Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc., give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.

In regression problems, we do not have such inconsistencies in output. The output is always continuous in nature and requires no further treatment.

Illustrative Example

For a classification model evaluation metric discussion, I have used my predictions for the problem BCI challenge on Kaggle. The solution to the problem is out of the scope of our discussion here. However, the final predictions on the training set have been used for this article. The predictions made for this problem were probability outputs which have been converted to class outputs assuming a threshold of 0.5.

Confusion Matrix

A confusion matrix is an N X N matrix, where N is the number of predicted classes. For the problem in hand, we have N=2, and hence we get a 2 X 2 matrix. It is a performance measurement for machine learning classification problems where the output can be two or more classes. It is a table with 4 different combinations of predicted and actual values. It is extremely useful for measuring precision-recall, Specificity, Accuracy, and most importantly, AUC-ROC curves.

Here are a few definitions you need to remember for a confusion matrix:

True Positive: You predicted positive, and it’s true.

True Negative: You predicted negative, and it’s true.

False Positive: (Type 1 Error): You predicted positive, and it’s false.

False Negative: (Type 2 Error): You predicted negative, and it’s false.

Accuracy: the proportion of the total number of correct predictions that were correct.

Positive Predictive Value or Precision: the proportion of positive cases that were correctly identified.

Negative Predictive Value: the proportion of negative cases that were correctly identified.

Sensitivity or Recall: the proportion of actual positive cases which are correctly identified.

Specificity: the proportion of actual negative cases which are correctly identified.

Rate: It is a measuring factor in a confusion matrix. It has also 4 types TPR, FPR, TNR, and FNR.

The accuracy for the problem in hand comes out to be 88%. As you can see from the above two tables, the Positive Predictive Value is high, but the negative predictive value is quite low. The same holds for Sensitivity and Specificity. This is primarily driven by the threshold value we have chosen. If we decrease our threshold value, the two pairs of starkly different numbers will come closer.

In general, we are concerned with one of the above-defined metrics. For instance, in a pharmaceutical company, they will be more concerned with a minimal wrong positive diagnosis. Hence, they will be more concerned about high Specificity. On the other hand, an attrition model will be more concerned with Sensitivity. Confusion matrices are generally used only with class output models.

F1 Score

In the last section, we discussed precision and recall for classification problems and also highlighted the importance of choosing a precision/recall basis for our use case. What if, for a use case, we are trying to get the best precision and recall at the same time? F1-Score is the harmonic mean of precision and recall values for a classification problem. The formula for F1-Score is as follows:

Now, an obvious question that comes to mind is why you are taking a harmonic mean and not an arithmetic mean. This is because HM punishes extreme values more. Let us understand this with an example. We have a binary classification model with the following results:

Precision: 0, Recall: 1

Here, if we take the arithmetic mean, we get 0.5. It is clear that the above result comes from a dumb classifier that ignores the input and predicts one of the classes as output. Now, if we were to take HM, we would get 0 which is accurate as this model is useless for all purposes.

This seems simple. There are situations, however, for which a data scientist would like to give a percentage more importance/weight to either precision or recall. Altering the above expression a bit such that we can include an adjustable parameter beta for this purpose, we get:

Fbeta measures the effectiveness of a model with respect to a user who attaches β times as much importance to recall as precision.

Gain and Lift Charts

Gain and Lift charts are mainly concerned with checking the rank ordering of the probabilities. Here are the steps to build a Lift/Gain chart:

You will get the following table from which you need to plot Gain/Lift charts:

This is a very informative table. The cumulative Gain chart is the graph between Cumulative %Right and Cumulative %Population. For the case in hand, here is the graph:

This graph tells you how well is your model segregating responders from non-responders. For example, the first decile, however, has 10% of the population, has 14% of the responders. This means we have a 140% lift at the first decile.

What is the maximum lift we could have reached in the first decile? From the first table of this article, we know that the total number of responders is 3850. Also, the first decile will contain 543 observations. Hence, the maximum lift at the first decile could have been 543/3850 ~ 14.1%. Hence, we are quite close to perfection with this model.

Let’s now plot the lift curve. The lift curve is the plot between total lift and %population. Note that for a random model, this always stays flat at 100%. Here is the plot for the case in hand:

You can also plot decile-wise lift with decile number:

What does this graph tell you? It tells you that our model does well till the 7th decile. Post which every decile will be skewed towards non-responders. Any model with lift @ decile above 100% till minimum 3rd decile and maximum 7th decile is a good model. Else you might consider oversampling first.

Lift / Gain charts are widely used in campaign targeting problems. This tells us to which decile we can target customers for a specific campaign. Also, it tells you how much response you expect from the new target base.

Kolomogorov Smirnov Chart

K-S or Kolmogorov-Smirnov chart measures the performance of classification models. More accurately, K-S is a measure of the degree of separation between the positive and negative distributions. The K-S is 100 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives.

On the other hand, If the model cannot differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. The K-S would be 0. In most classification models, the K-S will fall between 0 and 100, and the higher the value, the better the model is at separating the positive from negative cases.

For the case in hand, the following is the table:

We can also plot the %Cumulative Good and Bad to see the maximum separation. Following is a sample plot:

The evaluation metrics covered here are mostly used in classification problems. So far, we’ve learned about the confusion matrix, lift and gain chart, and kolmogorov-smirnov chart. Let’s proceed and learn a few more important metrics.

Area Under the ROC Curve (AUC – ROC)

Let’s first try to understand what the ROC (Receiver operating characteristic) curve is. If we look at the confusion matrix below, we observe that for a probabilistic model, we get different values for each metric.

Hence, for each sensitivity, we get a different specificity. The two vary as follows:

The ROC curve is the plot between sensitivity and (1- specificity). (1- specificity) is also known as the false positive rate, and sensitivity is also known as the True Positive rate. Following is the ROC curve for the case in hand.

Let’s take an example of threshold = 0.5 (refer to confusion matrix). Here is the confusion matrix:

As you can see, the sensitivity at this threshold is 99.6%, and the (1-specificity) is ~60%. This coordinate becomes on point in our ROC curve. To bring this curve down to a single number, we find the area under this curve (AUC).

Note that the area of the entire square is 1*1 = 1. Hence AUC itself is the ratio under the curve and the total area. For the case in hand, we get AUC ROC as 96.4%. Following are a few thumb rules:

.90-1 = excellent (A)

.80-.90 = good (B)

.70-.80 = fair (C)

.60-.70 = poor (D)

.50-.60 = fail (F)

We see that we fall under the excellent band for the current model. But this might simply be over-fitting. In such cases, it becomes very important to do in-time and out-of-time validations.

Points to Remember:

1. For a model which gives class as output will be represented as a single point in the ROC plot.

2. Such models cannot be compared with each other as the judgment needs to be taken on a single metric and not using multiple metrics. For instance, a model with parameters (0.2,0.8) and a model with parameters (0.8,0.2) can be coming out of the same model; hence these metrics should not be directly compared.

3. In the case of the probabilistic model, we were fortunate enough to get a single number which was AUC-ROC. But still, we need to look at the entire curve to make conclusive decisions. It is also possible that one model performs better in some regions and other performs better in others.

Advantages of Using ROC

Why should you use ROC and not metrics like the lift curve?

Lift is dependent on the total response rate of the population. Hence, if the response rate of the population changes, the same model will give a different lift chart. A solution to this concern can be a true lift chart (finding the ratio of lift and perfect model lift at each decile). But such a ratio rarely makes sense for the business.

The ROC curve, on the other hand, is almost independent of the response rate. This is because it has the two axes coming out from columnar calculations of the confusion matrix. The numerator and denominator of both the x and y axis will change on a similar scale in case of a response rate shift.

Log Loss

AUC ROC considers the predicted probabilities for determining our model’s performance. However, there is an issue with AUC ROC, it only takes into account the order of probabilities, and hence it does not take into account the model’s capability to predict a higher probability for samples more likely to be positive. In that case, we could use the log loss, which is nothing but a negative average of the log of corrected predicted probabilities for each instance.

p(yi) is the predicted probability of a positive class

1-p(yi) is the predicted probability of a negative class

yi = 1 for the positive class and 0 for the negative class (actual values)

Let us calculate log loss for a few random values to get the gist of the above mathematical function:

Log loss(1, 0.1) = 2.303

Log loss(1, 0.5) = 0.693

Log loss(1, 0.9) = 0.105

If we plot this relationship, we will get a curve as follows:

It’s apparent from the gentle downward slope towards the right that the Log Loss gradually declines as the predicted probability improves. Moving in the opposite direction, though, the Log Loss ramps up very rapidly as the predicted probability approaches 0.

So, the lower the log loss, the better the model. However, there is no absolute measure of a good log loss, and it is use-case/application dependent.

Whereas the AUC is computed with regards to binary classification with a varying decision threshold, log loss actually takes the “certainty” of classification into account.

Gini Coefficient

The Gini coefficient is sometimes used in classification problems. The Gini coefficient can be derived straight away from the AUC ROC number. Gini is nothing but the ratio between the area between the ROC curve and the diagonal line & the area of the above triangle. Following are the formulae used:

Gini = 2*AUC – 1

Gini above 60% is a good model. For the case in hand, we get Gini as 92.7%.

Concordant – Discordant Ratio

This is, again, one of the most important evaluation metrics for any classification prediction problem. To understand this, let’s assume we have 3 students who have some likelihood of passing this year. Following are our predictions:

Now picture this. if we were to fetch pairs of two from these three students, how many pairs would we have? We will have 3 pairs: AB, BC, and CA. Now, after the year ends, we see that A and C passed this year while B failed. No, we choose all the pairs where we will find one responder and another non-responder. How many such pairs do we have?

We have two pairs AB and BC. Now for each of the 2 pairs, the concordant pair is where the probability of the responder was higher than the non-responder. Whereas discordant pair is where the vice-versa holds true. In case both the probabilities were equal, we say it’s a tie. Let’s see what happens in our case :

Hence, we have 50% of concordant cases in this example. A concordant ratio of more than 60% is considered to be a good model. This metric generally is not used when deciding how many customers to target etc. It is primarily used to access the model’s predictive power. Decisions like how many to target are again taken by KS / Lift charts.

Root Mean Squared Error (RMSE)

RMSE is the most popular evaluation metric used in regression problems. It follows an assumption that errors are unbiased and follow a normal distribution. Here are the key points to consider on RMSE:

The power of ‘square root’ empowers this metric to show large number deviations.

The ‘squared’ nature of this metric helps to deliver more robust results, which prevent canceling the positive and negative error values. In other words, this metric aptly displays the plausible magnitude of the error term.

It avoids the use of absolute error values, which is highly undesirable in mathematical calculations.

When we have more samples, reconstructing the error distribution using RMSE is considered to be more reliable.

RMSE is highly affected by outlier values. Hence, make sure you’ve removed outliers from your data set prior to using this metric.

As compared to mean absolute error, RMSE gives higher weightage and punishes large errors.

RMSE metric is given by:

where N is the Total Number of Observations.

Root Mean Squared Logarithmic Error

In the case of Root mean squared logarithmic error, we take the log of the predictions and actual values. So basically, what changes are the variance that we are measuring? RMSLE is usually used when we don’t want to penalize huge differences in the predicted and the actual values when both predicted, and true values are huge numbers.

If both predicted and actual values are small: RMSE and RMSLE are the same.

R-Squared/Adjusted R-Squared

We learned that when the RMSE decreases, the model’s performance will improve. But these values alone are not intuitive.

In the case of a classification problem, if the model has an accuracy of 0.8, we could gauge how good our model is against a random model, which has an accuracy of 0.5. So the random model can be treated as a benchmark. But when we talk about the RMSE metrics, we do not have a benchmark to compare.

This is where we can use the R-Squared metric. The formula for R-Squared is as follows:

MSE(model): Mean Squared Error of the predictions against the actual values

MSE(baseline): Mean Squared Error of mean prediction against the actual values

In other words, how good is our regression model as compared to a very simple model that just predicts the mean value of the target from the train set as predictions?

Adjusted R-Squared

A model performing equal to the baseline would give R-Squared as 0. Better the model, the higher the r2 value. The best model with all correct predictions would give R-Squared of 1. However, on adding new features to the model, the R-Squared value either increases or remains the same. R-Squared does not penalize for adding features that add no value to the model. So an improved version of the R-Squared is the adjusted R-Squared. The formula for adjusted R-Squared is given by:

k: number of features

n: number of samples

As you can see, this metric takes the number of features into account. When we add more features, the term in the denominator n-(k +1) decreases, so the whole expression increases.

If R-Squared does not increase, that means the feature added isn’t valuable for our model. So overall, we subtract a greater value from 1 and adjusted r2, in turn, would decrease.

Beyond these 12 evaluation metrics, there is another method to check the model performance. These 7 methods are statistically prominent in data science. But, with the arrival of machine learning, we are now blessed with more robust methods of model selection. Yes! I’m talking about Cross Validation.

Though cross-validation isn’t really an evaluation metric that is used openly to communicate model accuracy, the result of cross-validation provides a good enough intuitive result to generalize the performance of a model.

Let’s now understand cross-validation in detail.

Cross Validation

Let’s first understand the importance of cross-validation. Due to my busy schedule these days, I don’t get much time to participate in data science competitions. A long time back, I participated in TFI Competition on Kaggle. Without delving into my competition performance, I would like to show you the dissimilarity between my public and private leaderboard scores.

Here Is an Example of Scoring on Kaggle!

For the TFI competition, the following were three of my solution and scores (the lesser, the better):

Over-fitting is nothing, but when your model becomes highly complex that it starts capturing noise, also. This ‘noise’ adds no value to the model but only inaccuracy.

In the following section, I will discuss how you can know if a solution is an over-fit or not before we actually know the test set results.

The Concept of Cross-Validation

Cross Validation is one of the most important concepts in any type of data modeling. It simply says, try to leave a sample on which you do not train the model and test the model on this sample before finalizing the model.

The above diagram shows how to validate the model with the in-time sample. We simply divide the population into 2 samples and build a model on one sample. The rest of the population is used for in-time validation.

Could there be a negative side to the above approach?

I believe a negative side of this approach is that we lose a good amount of data from training the model. Hence, the model is very high bias. And this won’t give the best estimate for the coefficients. So what’s the next best option?

What if we make a 50:50 split of the training population and the train on the first 50 and validate on the rest 50? Then, we train on the other 50 and test on the first 50. This way, we train the model on the entire population, however, on 50% in one go. This reduces bias because of sample selection to some extent but gives a smaller sample to train the model on. This approach is known as 2-fold cross-validation.

K-Fold Cross-Validation

Let’s extrapolate the last example to k-fold from 2-fold cross-validation. Now, we will try to visualize how a k-fold validation work.

This is a 7-fold cross-validation.

Here’s what goes on behind the scene: we divide the entire population into 7 equal samples. Now we train models on 6 samples (Green boxes) and validate on 1 sample (grey box). Then, at the second iteration, we train the model with a different sample held as validation. In 7 iterations, we have basically built a model on each sample and held each of them as validation. This is a way to reduce the selection bias and reduce the variance in prediction power. Once we have all 7 models, we take an average of the error terms to find which of the models is best.

How does this help to find the best (non-over-fit) model?

k-fold cross-validation is widely used to check whether a model is an overfit or not. If the performance metrics at each of the k times modeling are close to each other and the mean of the metric is highest. In a Kaggle competition, you might rely more on the cross-validation score than the Kaggle public score. This way, you will be sure that the Public score is not just by chance.

How do we implement k-fold with any model?

Coding k-fold in R and Python are very similar. Here is how you code a k-fold in Python:

Try out the code for KFold in the live coding window below:

But how do we choose k?

Think of extreme cases:

Generally, a value of k = 10 is recommended for most purposes.


Measuring the performance of the training sample is pointless. And leaving an in-time validation batch aside is a waste of data. K-Fold gives us a way to use every single data point, which can reduce this selection bias to a good extent. Also, K-fold cross-validation can be used with any modeling technique.

In addition, the metrics covered in this article are some of the most used metrics of evaluation in classification and regression problems.

Key Takeaways

Evaluation metrics measure the quality of the machine learning model.

For any project evaluating machine learning models or algorithms is essential.

Frequently Asked Questions

Q1. What are the 3 metrics of evaluation?

A. Accuracy, confusion matrix, log-loss, and AUC-ROC are the most popular evaluation metrics.

Q2. What are evaluation metrics in machine learning?

A. Evaluation metrics quantify the performance of a machine learning model. It involves training a model and then comparing the predictions to expected values.

Q3. What are the 4 metrics for evaluating classifier performance?

A. Accuracy, confusion matrix, log-loss, and AUC-ROC are the most popular evaluation metrics used for evaluating classifier performance.


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