Menjelajahi data UCI SECOM

Untuk menutup bab ini dan memperkuat pemahaman Anda tentang bagging, kini saatnya bekerja dengan himpunan data baru! Data ini berasal dari proses manufaktur semikonduktor, diperoleh dari UCI Machine Learning Repository.

Setiap baris merepresentasikan satu entitas produksi. Fitur adalah pengukuran dari sensor atau titik dalam proses. Label menunjukkan apakah entitas lulus (1) atau gagal (-1) dalam pengujian.

Himpunan data telah dimuat dan tersedia sebagai uci_secom. Variabel target adalah kolom 'Pass/Fail'. Gunakan metode .value_counts() dan .describe() untuk memeriksa variabel ini. Apa yang Anda amati?

Latihan ini adalah bagian dari kursus

Metode Ensemble di Python

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Latihan ini adalah bagian dari kursus

Metode Ensemble di Python

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Mulai Kursus Gratis

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: Kekuatan model "lemah"Exercise 2: Pohon keputusan terbatas dan tidak terbatas Exercise 3: Pohon keputusan "lemah"Exercise 4: Bootstrap aggregating Exercise 5: Pelatihan dengan bootstrapping Exercise 6: Upaya pertama melakukan bagging Exercise 7: BaggingClassifier: seluk-beluknya Exercise 8: Bagging: cara scikit-learn Exercise 9: Memeriksa out-of-bag score Exercise 10: Parameter bagging: kiat dan trik Exercise 11: Menjelajahi data UCI SECOM

Latihan Saat Ini

Exercise 12: Model bagging yang lebih kompleks Exercise 13: Menyetel hyperparameter bagging

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