Google-appgegevens verkennen

De eerste gegevensset waarmee je werkt bevat informatie over de beoordelingen van ongeveer 10.800 apps in de Google Play Store. Deze is vooraf geladen in een DataFrame genaamd ratings. Er zijn 13 features die een app beschrijven, zoals 'Category' en 'Price'. Het doel is om deze informatie te gebruiken om de beoordeling van een app te voorspellen, die kan variëren van 1 tot 5.

Voordat je dat doet, is het een goed idee om vertrouwd te raken met de gegevensset met pandas-methoden zoals .head() en .describe(). Verken de data in de IPython Shell en kies de onjuiste uitspraak uit de opties hieronder.

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Ensemblemethoden in Python

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Ensemblemethoden 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: Introductie tot ensemblemethoden Exercise 2: Google-appgegevens verkennen

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Exercise 3: De beoordeling van een app voorspellen Exercise 4: Voting Exercise 5: Het beste model kiezen Exercise 6: Je eerste ensemble samenstellen Exercise 7: Je ensemble evalueren Exercise 8: Gemiddelden Exercise 9: Reis naar Westeros Exercise 10: Voorspellen wie er sterft in GoT 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 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