Kennismaking met de filmdatabase

In dit hoofdstuk werk je met TMDb (The Movie Database). Deze bevat metadata van ongeveer 5000 films.

De gegevensset is geladen en beschikbaar als movies.

Je hoofddoel is om de filmopbrengst te voorspellen – specifieker: de log-opbrengst, de genormaliseerde versie van de feature revenue.

Gebruik de methode .describe() om de variabele target te verkennen, die de waarden bevat die in het histogram zijn weergegeven. Je kunt ook het histogram rechts bekijken. Wat kun je concluderen?

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Ensemblemethoden in Python

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Zet theorie om in actie met een van onze interactieve oefeningen.

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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: 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: De effectiviteit van geleidelijk leren Exercise 2: Kennismaking met de filmdatabase

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Exercise 3: Filmeigenschappen verkennen Exercise 4: Filmomzet voorspellen Exercise 5: Boosting voor voorspelde omzet Exercise 6: Adaptive boosting: bekroond model Exercise 7: Je eerste AdaBoost-model Exercise 8: Boomgebaseerde AdaBoost-regressie Exercise 9: Haal alles uit AdaBoost Exercise 10: Gradient boosting Exercise 11: Google-apprecensies opnieuw bekijken Exercise 12: Sentimentanalyse met GBM Exercise 13: Verschillende smaken van gradient boosting Exercise 14: Filmomzet voorspellen met CatBoost Exercise 15: Boosting-wedstrijd: 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