Exploring movie features

In the rest of this chapter, you will use boosting algorithms to build models that predict the log-revenue of movies based on the following features: 'budget', 'popularity', 'runtime', 'vote_average', and 'vote_count'. There are many more features, but these will be of primary interest. Having explored the target in the previous exercise, it's now time to explore these features!

The features of interest have been loaded into the features variable. Use the .describe() method to explore it. What do you notice?

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Ensemble Methods in Python

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Ensemble Methods in Python

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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!

Exercise 1: Introduction to ensemble methods Exercise 2: Exploring Google apps data Exercise 3: Predicting the rating of an app Exercise 4: Voting Exercise 5: Choosing the best model Exercise 6: Assembling your first ensemble Exercise 7: Evaluating your ensemble Exercise 8: Averaging Exercise 9: Journey to Westeros Exercise 10: Predicting GoT deaths Exercise 11: Soft vs. hard voting

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.

Exercise 1: The strength of “weak” models Exercise 2: Restricted and unrestricted decision trees Exercise 3: "Weak" decision tree Exercise 4: Bootstrap aggregating Exercise 5: Training with bootstrapping Exercise 6: A first attempt at bagging Exercise 7: BaggingClassifier: nuts and bolts Exercise 8: Bagging: the scikit-learn way Exercise 9: Checking the out-of-bag score Exercise 10: Bagging parameters: tips and tricks Exercise 11: Exploring the UCI SECOM data Exercise 12: A more complex bagging model Exercise 13: Tuning bagging hyperparameters

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.

Exercise 1: The effectiveness of gradual learning Exercise 2: Introducing the movie database Exercise 3: Exploring movie features

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Exercise 4: Predicting movie revenue Exercise 5: Boosting for predicted revenue Exercise 6: Adaptive boosting: award winning model Exercise 7: Your first AdaBoost model Exercise 8: Tree-based AdaBoost regression Exercise 9: Making the most of AdaBoost Exercise 10: Gradient boosting Exercise 11: Revisiting Google app reviews Exercise 12: Sentiment analysis with GBM Exercise 13: Gradient boosting flavors Exercise 14: Movie revenue prediction with CatBoost Exercise 15: Boosting contest: Light vs Extreme

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.

Exercise 1: The intuition behind stacking Exercise 2: Exploring the mushroom dataset Exercise 3: Predicting mushroom edibility Exercise 4: K-nearest neighbors for mushrooms Exercise 5: Build your first stacked ensemble Exercise 6: Applying stacking to predict app ratings Exercise 7: Building the stacking classifier Exercise 8: Stacked predictions for app ratings Exercise 9: Let's mlxtend it!Exercise 10: A first attempt with mlxtend Exercise 11: Back to regression with stacking Exercise 12: Mushrooms: a matter of life or death Exercise 13: Ensembling it all together