Feature union in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class used to combine features.

ML Python

from sklearn.pipeline import [1]

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AStandardScaler

BPCA

CFeatureUnion

DGridSearchCV

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated classes like StandardScaler or PCA.

Confusing FeatureUnion with Pipeline.

FeatureUnion is the class used to combine multiple feature extraction processes into one.

2fill in blank

medium

Complete the code to create a FeatureUnion combining two transformers.

ML Python

union = FeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', [1]())])

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AStandardScaler

BMinMaxScaler

COneHotEncoder

DPolynomialFeatures

Attempts:

3 left

💡 Hint

Common Mistakes

Using OneHotEncoder which is for categorical data encoding.

Using PolynomialFeatures which creates new features.

StandardScaler is commonly used to scale features before combining them.

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hard

Fix the error in the code to correctly fit the FeatureUnion on data X.

ML Python

union = FeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', StandardScaler())])
union.[1](X)

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Apredict

Bfit_transform

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform() before fitting causes errors.

Using predict() which is for models, not transformers.

To prepare the FeatureUnion for transformation, we first call fit on the data.

4fill in blank

hard

Fill both blanks to create a pipeline that first applies FeatureUnion then fits a classifier.

ML Python

pipeline = Pipeline([('features', [1]), ('clf', [2])])

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AFeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', StandardScaler())])

BLogisticRegression()

CRandomForestClassifier()

DStandardScaler()

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler as the classifier step.

Using RandomForestClassifier when LogisticRegression is expected.

FeatureUnion combines features, then LogisticRegression fits the combined features.

5fill in blank

hard

Fill all three blanks to create a FeatureUnion with two transformers and fit_transform on data X.

ML Python

union = FeatureUnion(transformer_list=[('pca', [1]), ('scaler', [2])])
X_transformed = union.[3](X)

Drag options to blanks, or click blank then click option'

APCA(n_components=3)

BStandardScaler()

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of fit_transform for transformation.

Mixing up transformer instances with method names.

PCA and StandardScaler are transformers; fit_transform fits and transforms the data.