scikit-learn Pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Pipeline class from scikit-learn.

ML Python

from sklearn.pipeline import [1]

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APipeline

Bpipe

CPipeLine

Dpipeline

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'pipeline' instead of 'Pipeline'.

Misspelling the class name with wrong capitalization.

The correct class name is Pipeline with a capital P.

2fill in blank

medium

Complete the code to create a Pipeline with a scaler and a logistic regression model.

ML Python

pipeline = Pipeline(steps=[('scaler', [1]()), ('model', LogisticRegression())])

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AstandardScaler

BScalerStandard

CStandardScaler

Dscale

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect class names like 'ScalerStandard' or lowercase variants.

Forgetting to import StandardScaler before using it.

The correct scaler class is StandardScaler from scikit-learn.

3fill in blank

hard

Fix the error in the code to fit the pipeline on training data X_train and y_train.

ML Python

pipeline.[1](X_train, y_train)

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Afit

Bfit_transform

Ctransform

Dtrain

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' which is not a method in scikit-learn.

Using 'transform' which does not accept labels.

The method to train the pipeline is fit.

4fill in blank

hard

Fill both blanks to create a pipeline that first applies PCA and then a classifier.

ML Python

pipeline = Pipeline(steps=[('pca', [1](n_components=2)), ('clf', [2]())])

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APCA

BLogisticRegression

CRandomForestClassifier

DStandardScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler instead of PCA for the first step.

Using LogisticRegression when the question expects RandomForestClassifier.

The first step is PCA for dimensionality reduction, and the second is RandomForestClassifier as the model.

5fill in blank

hard

Fill all three blanks to create a pipeline that scales data, reduces dimensions, and classifies.

ML Python

pipeline = Pipeline(steps=[('scale', [1]()), ('reduce', [2](n_components=3)), ('classify', [3]())])

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

AStandardScaler

BPCA

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of steps in the pipeline.

Using KNeighborsClassifier instead of LogisticRegression as classifier.

The pipeline first scales data with StandardScaler, then reduces dimensions with PCA, and finally classifies with LogisticRegression.