Go Programming

Go Programming

1594305120

You’ve fit the model, what’s next? — Classification Edition

From there you commit to your GitHub repository, update whatever Kaggle notebook you were on, close your computer, pat yourself on the back, and call it a productive day! ……Well, I wish data science was that straightforward.

However, there is a question that you need to ask yourself though. Is this model any good?

In this post, we will be going through a quick overview of some common classification evaluation metrics to help us determine how well our model performed. For simplicity’s sake, let’s imagine we’re dealing with a binary classification problem, such as fraud detection and food recall.

Before we start, repeat after me. “There is no single evaluation metric that is “on the money” for any classification problem or any other problem.”

Great, now repeat this mantra whenever you’re are evaluating your models.

Okay great, let’s start with the simplest metric available being “accuracy”. It’s popular and very easy to measure. But it’s too simplistic, and it doesn’t really tell us the full picture. Now repeat the mantra again. “There is no single evaluation metric that is “on the money” for any classification problem or any other problem.”

Let me give you an example, say you have data set comprising 1000 data points, with 950 being class 0, and 50 being class 1. Assuming class 0 represents correctly printed books, and class 1 represents books that are faulty, let’s assume the faultiness was that they printed it upside down. We create a simple model and behold our accuracy is 95%.

With little knowledge of the context, we might tell ourselves “This is a great model! Let’s launch this thing into production!” However, because of this highly imbalanced data set, our model may have just classified everything as class 0, misclassifying our faulty book, thus our accuracy is 95% having all our class 0 correctly classified. We can quickly check if this is true by calculating the “null accuracy” which refers to the accuracy that is achieved by always predicting the most frequent class.

Now repeat after me. “There is no single evaluation metric that is “on the money” for any classification problem, or any other problem.”

To dig into our accuracy score and break it down further, one tool that many data scientist like to use is the confusion matrix. It’s not an evaluation metric, but it does the job at highlighting the “types” of errors our classifier is making for each class.

Image for post

Let’s image our predicted values as our model’s actual prediction performance, with the top row having our model predicting that data point as positive, and bottom row having our model predicting that data point as negative.

  • True Positive (TP) refers to predicting a positive class as positive. Meaning it correctly classified it.
  • False Positive( FP) refers to predicting a negative class as positive. Meaning it failed to classify it properly.
  • False Negative (FN) refers to predicting a positive class as negative. Meaning it failed to classify it properly.
  • True Negative (TN) refers to predicting the negative class as negative. Meaning it correctly classified it.

Going back to our faulty book example, our model confusion matrix would have looked like this.

Image for post

#evaluation-metric #statistics #confusion-matrix #data-science #towards-data-science

What is GEEK

Buddha Community

You’ve fit the model, what’s next? — Classification Edition
Meryem Rai

Meryem Rai

1626713387

Fitness App Development: How to Create a Best Fitness App?

There’s a large body of research about the necessity of fitness to have great outcomes for mental health, cardiovascular health, longevity, etc. The rise of sedentary lifestyles makes it vital for people to add fitness routines to their daily schedules. Thus, fitness videos and apps have become popular making fitness accessible. Especially during the 2020-21 pandemic, sports and fitness studios were able to reach clients only through fitness apps.As a fitness instructor, creating a fitness streaming app development gives you credibility, direct connection to clients, and independence. Read on to find out how you can build a fitness app.

Why create the best fitness app?

Technology is evolving at a fast pace. After internet speeds picked up, video streaming websites leveraged it; also smartphones and tablets became ubiquitous, allowing everyone to watch videos on the go. Finally, since 2014, wearables became popular purchases and interest in fitness apps shot up.

Online fitness platform gyms have successfully combined the offline and online experience. VPlayed fitness app building service offers core features for this. It is scalable and easy to integrate while carrying your brand name.

Engaging services to be provided by fitness apps
Fitness apps have more features than ever and you should consider adding the following to yours:

Diet plan

This feature tracks the food consumed by the user, the nutritional breakup, and may also suggest complete meals based on preferences. There can also be a water intake tracker.

Activity tracking

This feature is crucial especially when the app can integrate with wearables. They use smartphone features to calculate distance traveled, steps taken, heart rate, and even the timings and duration of the activity, throughout the day.

Top Key features in online fitness App
After seeing the top functions users appreciate, it’s time to see what a fitness app’s features should be.

Personal Account

Users should be able to log in especially if you need to collect user data to make the app experience more personalized.

Third-Party Device Connectivity

Users will want their wearables and other devices like tablets and smartwatches to be connected, requiring IoT hybrid solutions.

User Activity Tracking

Since fitness apps are expected to track users’ progress over time, the activity-tracking feature is crucial. The data can be gathered globally, i.e over a period or locally, i.e from session to session.

How to monetize your fitness app?

There are a few different ways to monetize your fitness video content . Choose one based on the number of users and the potential for scalability.

Subscription-based Video on Demand
In this model people sign up for packages that give them access to some or all of the app content for a fixed period. The more features you offer, the more you can earn from this option.

Pay Per View

This option is useful for live-streaming fitness classes to new users who want to test the app. You may also combine it with SVOD.

Advertisement-based Video on Demand

Here your app content is available in exchange for viewers watching ads. This option works out well if you have enough users and infrequent uploads.

Coupons & Promotions

Having coupons and promotions in addition to one or more of the above monetization options helps attract new users or gets old users to sign up for more features.

Conclusion

Many top fitness experts have established trademark routines and launched fitness streaming platforms to control their revenue streams. Choosing a white-label fitness app development solution saves the trouble of hiring multiple teams and gives you all the fitness app documentation. Options like VPlayed give you good tech support as well, freeing you to focus on making unique content.

#fitness app #fitness app development #how to create fitness app #fitness streaming #online fitness platform

Go Programming

Go Programming

1594305120

You’ve fit the model, what’s next? — Classification Edition

From there you commit to your GitHub repository, update whatever Kaggle notebook you were on, close your computer, pat yourself on the back, and call it a productive day! ……Well, I wish data science was that straightforward.

However, there is a question that you need to ask yourself though. Is this model any good?

In this post, we will be going through a quick overview of some common classification evaluation metrics to help us determine how well our model performed. For simplicity’s sake, let’s imagine we’re dealing with a binary classification problem, such as fraud detection and food recall.

Before we start, repeat after me. “There is no single evaluation metric that is “on the money” for any classification problem or any other problem.”

Great, now repeat this mantra whenever you’re are evaluating your models.

Okay great, let’s start with the simplest metric available being “accuracy”. It’s popular and very easy to measure. But it’s too simplistic, and it doesn’t really tell us the full picture. Now repeat the mantra again. “There is no single evaluation metric that is “on the money” for any classification problem or any other problem.”

Let me give you an example, say you have data set comprising 1000 data points, with 950 being class 0, and 50 being class 1. Assuming class 0 represents correctly printed books, and class 1 represents books that are faulty, let’s assume the faultiness was that they printed it upside down. We create a simple model and behold our accuracy is 95%.

With little knowledge of the context, we might tell ourselves “This is a great model! Let’s launch this thing into production!” However, because of this highly imbalanced data set, our model may have just classified everything as class 0, misclassifying our faulty book, thus our accuracy is 95% having all our class 0 correctly classified. We can quickly check if this is true by calculating the “null accuracy” which refers to the accuracy that is achieved by always predicting the most frequent class.

Now repeat after me. “There is no single evaluation metric that is “on the money” for any classification problem, or any other problem.”

To dig into our accuracy score and break it down further, one tool that many data scientist like to use is the confusion matrix. It’s not an evaluation metric, but it does the job at highlighting the “types” of errors our classifier is making for each class.

Image for post

Let’s image our predicted values as our model’s actual prediction performance, with the top row having our model predicting that data point as positive, and bottom row having our model predicting that data point as negative.

  • True Positive (TP) refers to predicting a positive class as positive. Meaning it correctly classified it.
  • False Positive( FP) refers to predicting a negative class as positive. Meaning it failed to classify it properly.
  • False Negative (FN) refers to predicting a positive class as negative. Meaning it failed to classify it properly.
  • True Negative (TN) refers to predicting the negative class as negative. Meaning it correctly classified it.

Going back to our faulty book example, our model confusion matrix would have looked like this.

Image for post

#evaluation-metric #statistics #confusion-matrix #data-science #towards-data-science

Bella Garvin

Bella Garvin

1613621892

Fitness App Development

https://www.orbitedgetech.com/fitness-app-development/

Orbit Edge is one of the leading fitness app development companies that designs a healthcare app with a wide range of health related solutions. Team Orbit Edge focuses on quality so that users can easily navigate through the app interface. Dedicated developers provide their 100% to deliver a secure app that has come up with all the essential compliances.

#fitness app development #fitness app development company #fitness app development services #hire fitness app developers #build fitness app

Mckenzie  Osiki

Mckenzie Osiki

1622134500

Inside MoveNet, Google’s Latest Pose Detection Model

Ahead of Google I/O, Google Research launched a new pose detection model in TensorFlow.js called MoveNet. This ultra-fast and accurate model can detect 17 key points in the human body. MoveNet is currently available on TF Hub with two variants — Lightning and Thunder.

While Lightning is intended for latency-critical applications, Thunder is for applications that call for higher accuracy. Both models claim to run faster than real-time (30+ frames per second (FPS)) on most personal computers, laptops and phones.

The model can be launched in the browser using TensorFlow.js architecture with no server calls needed after the initial page load or external packages. The live demo version is available here.

Currently, the MoveNet model works for the individual in the camera field-of-view. But, soon, Google Research looks to extend the MoveNet model to the multi-person domain so that developers can support applications with multiple people.

#developers corner #body movements online #body movements virtual #fitness machine learning #google i/o #google latest #google new development #google research latest #machine learning models body poses #ose detection model #remote healthcare solutions #tensorflow latest model #track body movements #wellness machine learning

Best Fitness and Health Mobile App Development Service

Are you looking to convert your fitness mobile app idea into a profitable business? AppClues Infotech is the best fitness & health mobile app Development Company that builds innovative, flawless user-friendly & user-centric mobile apps.

We make health and fitness mobile apps that are true to your health and fitness goals, with the right technology embedded. Hire our dedicated experts to fulfill your fitness mobile app requirements at an affordable price.

We provide Fitness & Health Mobile App Solutions:
• Develop Fitness Apps
• Fitness tracker app development
• Exercise Mobile App (Gyms, Yoga Session, Aerobics)
• Diet Planner App
• Mobile App for personal fitness trainers and more

For more info:
Call: +1-978-309-9910
Email: info@appcluesinfotech.com

#fitness mobile app development solution #top health and fitness app developers #cost to make a fitness app #fitness app development #fitness app development company