Random Forests are a bread and butter model/algorithm for machine learning. They were first described in their current form as recently as 2001 in a classic paper by the late Leo Breiman. Even with the rise in popularity of artificial neural networks, they find practical use in a variety of situations.
There are a wealth of resources detailing how Random Forests work. This article will very briefly review them before turning to the main focus: how to fit them faster with warm starts and out-of-bag cross-validation. With these two techniques, hyper-parameters selection can be sped up substantially, reducing fitting time.
#editors-pick #artificial-intelligence #machine-learning #data-science #towards-data-science
Random Forest is a mainstream AI algorithm that has a place with the regulated learning strategy. It might be used for both Classification and Regression issues in ML. It depends on the idea of ensemble learning, which is a cycle of joining numerous classifiers to tackle an intricate issue and to improve the presentation of the model.
As the name proposes, “Random Forest is a classifier that contains different decision trees on various subsets of the given dataset and takes the typical to improve the perceptive precision of that dataset.”
Instead of relying upon one decision tree, the random forest takes the figure from each tree and subject it to the larger part votes of desires, and it predicts the last yield. The more noticeable number of trees in the forest prompts higher exactness and forestalls the issue of overfitting.
Since the random forest consolidates various trees to anticipate the class of the dataset, it is conceivable that some choice trees may foresee the right yield, while others may not. Yet, together, all the trees anticipate the right yield. In this way, beneath are two presumptions for a superior random forest classifier:
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This article aims to demystify the popular random forest (here and throughout the text —** RF**) algorithm and show its principles by using graphs, code snippets and code outputs.
The full implementation of the RF algorithm written by me in python can be accessed via: https://github.com/Eligijus112/decision-tree-python
I highly encourage anyone who stumbled upon this article to dive deep into the code because the understanding of the code will make any future documentation reading about **RF **much more straightforward and less stressful.
Any suggestions about optimizations are highly encouraged and are welcomed via a pull request on GitHub.
The building blocks of RF are simple decision trees. This article will be much easier to read if the reader is familiar with the concept of a classification decision tree. It is highly recommended to go through the following article before going any further:
#coding #machine-learning #random-forest #python #python from scratch #random forest algorithm
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:
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.
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.
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.
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
SVOD platform 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.
Create the perfect on demand fitness streaming services just like Apple Fitness Plus, Peloton, CorePower on Demand, Sona Fitness, DailyBurn, Practice with Clara, YogaGlo and lots more for you to target like-minded fitness excersiers and generate best money.
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
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.
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There are two types of random number generators: pseudo-random number generator and true random number generator.
Pseudorandom numbers depend on computer algorithms. The computer uses algorithms to generate random numbers. These random numbers are not truly random because they are predictable like the generated numbers using NumPy random seed.
Whereas, truly random numbers are generated by measuring truly physical random parameters so we can ensure that the generated numbers are truly random.
The pseudo-random numbers are not safe to use in cryptography because they can be guessed by attackers.
In Python, the built-in random module generates pseudo-random numbers. In this tutorial, we will discuss both types. So let’s get started.
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#python #random #generate random numbers #random numbers #generate random numbers in python