Implementeer cross_val_score()

Je bedrijf heeft een paar nieuwe snoepjes ontwikkeld, maar weet nog niet of ze alle vijf op de markt moeten komen. Om de populariteit van deze nieuwe snoepjes te voorspellen, is aan jou gevraagd om een regressiemodel te bouwen met de candy-gegevensset. Denk eraan dat de responswaarde het head-to-head winstpercentage tegen andere snoepjes is.

Voordat je verschillende regressiemodellen gaat proberen, besluit je eerst cross-validatie uit te voeren op een simpel random forest-model om een basisfout te krijgen om toekomstige resultaten mee te vergelijken.

Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Oefeninstructies

Vul cross_val_score() in.
- Gebruik X_train als trainingsdata en y_train als respons.
- Gebruik rfc als model, 10-voudige cross-validatie en mse als scoringsfunctie.
Print het gemiddelde van de cv-resultaten.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

rfc = RandomForestRegressor(n_estimators=25, random_state=1111)
mse = make_scorer(mean_squared_error)

# Set up cross_val_score
cv = cross_val_score(estimator=____,
                     X=____,
                     y=____,
                     cv=____,
                     scoring=____)

# Print the mean error
print(cv.____())

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Deze oefening maakt deel uit van de cursus

Modelvalidatie in Python

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Begin de cursus gratis

Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I underfitting?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: De problemen met holdout-sets Exercise 2: Twee steekproeven Exercise 3: Mogelijke problemen Exercise 4: Cross-validatie Exercise 5: scikit-learn's KFold()Exercise 6: KFold-indices gebruiken Exercise 7: sklearns cross_val_score()Exercise 8: Methode’s van scikit-learn Exercise 9: Implementeer cross_val_score()

Huidige oefening

Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: Wanneer gebruik je LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!