SciPy with scikit-learn pipeline - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

SciPy

from sklearn.pipeline import [1]

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APipeline

Bpipeline

CPipe

DPipelines

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'pipeline' instead of 'Pipeline'.

Trying to import 'Pipe' or 'Pipelines' which do not exist.

The Pipeline class is imported with a capital P as Pipeline from sklearn.pipeline.

2fill in blank

medium

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

SciPy

from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

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

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AMinMaxScaler

BStandardScaler

CNormalizer

DRobustScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Using a scaler that does not standardize data like MinMaxScaler.

Forgetting to instantiate the scaler with parentheses.

The pipeline uses StandardScaler() to scale features before logistic regression.

3fill in blank

hard

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

SciPy

pipeline = Pipeline([('scaler', StandardScaler()), ('clf', LogisticRegression())])
pipeline.[1](X_train, y_train)

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Afit

Btransform

Cpredict

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' which only changes data but does not train.

Using 'predict' before training the model.

To train the pipeline, use the fit method with features and labels.

4fill in blank

hard

Fill both blanks to create a pipeline that scales data and then applies a KNeighborsClassifier.

SciPy

from sklearn.neighbors import [1]
pipeline = Pipeline([('scaler', StandardScaler()), ('knn', [2]())])

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AKNeighborsClassifier

BLogisticRegression

CRandomForestClassifier

DSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different classifier than the one used in the pipeline.

Using different classifiers in import and pipeline steps.

The KNeighborsClassifier is imported and used as the classifier in the pipeline.

5fill in blank

hard

Fill all three blanks to create a pipeline that scales data, applies PCA for dimensionality reduction, and then fits a logistic regression model.

SciPy

from sklearn.decomposition import [1]
from sklearn.linear_model import [2]
pipeline = Pipeline([('scaler', StandardScaler()), ('pca', [3](n_components=2)), ('clf', LogisticRegression())])

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

APCA

BLogisticRegression

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing PCA with other decomposition methods.

Using a different classifier than logistic regression.

The pipeline uses PCA for dimensionality reduction and LogisticRegression as the classifier.