PCA in a model pipeline

We just saw that legendary Pokemon tend to have higher stats overall. Let's see if we can add a classifier to our pipeline that detects legendary versus non-legendary Pokemon based on the principal components.

The data has been pre-loaded for you and split into training and tests datasets: X_train, X_test, y_train, y_test.

Same goes for all relevant packages and classes(Pipeline(), StandardScaler(), PCA(), RandomForestClassifier()).

Deze oefening maakt deel uit van de cursus

Dimensionality Reduction in Python

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Build the pipeline
pipe = Pipeline([
        ('scaler', ____),
        ('reducer', ____),
        ('classifier', ____)])

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