Ensemble Methods in Python
|
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.
Course Description
Continue your machine learning journey by diving into the wonderful world of ensemble learning methods! These are an exciting class of machine learning techniques that combine multiple individual algorithms to boost performance and solve complex problems at scale across different industries. Ensemble techniques regularly win online machine learning
What You’ll Learn
Combining Multiple Models
Do you struggle to determine which of the models you built is the best for your problem? You should give up on that, and use them all instead! In this chapter, you’ll learn how to combine multiple models into one using “Voting” and “Averaging”. You’ll use these to predict the ratings of apps on the Google Play Store, whether or not a Pokémon is legendary, and which characters are going to die in Game of Thrones!
Boosting
Boosting is class of ensemble learning algorithms that includes award-winning models such as AdaBoost. In this chapter, you’ll learn about this award-winning model, and use it to predict the revenue of award-winning movies! You’ll also learn about gradient boosting algorithms such as CatBoost and XGBoost.
Bagging
Bagging is the ensemble method behind powerful machine learning algorithms such as random forests. In this chapter you’ll learn the theory behind this technique and build your own bagging models using scikit-learn.
Stacking
Get ready to see how things stack up! In this final chapter you’ll learn about the stacking ensemble method. You’ll learn how to implement it using scikit-learn as well as with the mlxtend library! You’ll apply stacking to predict the edibility of North American mushrooms, and revisit the ratings of Google apps with this more advanced approach.
User Reviews
Be the first to review “Ensemble Methods in Python”
You must be logged in to post a review.
There are no reviews yet.