Pipeline best practices in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a simple pipeline that scales data and fits a model.

ML Python

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

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', [1]())
])

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AKNeighborsClassifier

BLogisticRegression

CRandomForestClassifier

DSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a model class that is not imported or not suitable for the pipeline step.

Forgetting to instantiate the model with parentheses.

The pipeline uses LogisticRegression as the classifier step. This fits the model after scaling the data.

2fill in blank

medium

Complete the code to split data into training and testing sets before building the pipeline.

ML Python

from sklearn.model_selection import [1]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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AKFold

Bcross_val_score

CGridSearchCV

Dtrain_test_split

Attempts:

3 left

💡 Hint

Common Mistakes

Using cross-validation functions instead of splitting data.

Not importing the correct function.

The function train_test_split is used to split data into training and testing sets.

3fill in blank

hard

Fix the error in the pipeline code by completing the missing step for feature selection.

ML Python

from sklearn.feature_selection import [1]

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('selector', SelectKBest(k=10)),
    ('classifier', LogisticRegression())
])

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AVarianceThreshold

BPCA

CSelectKBest

DRFE

Attempts:

3 left

💡 Hint

Common Mistakes

Using PCA which is a dimensionality reduction technique, not feature selection.

Using classes not imported or incompatible with the pipeline.

The feature selection step uses SelectKBest to select the top 10 features.

4fill in blank

hard

Fill both blanks to create a pipeline that scales data and performs cross-validation scoring.

ML Python

from sklearn.model_selection import [1]
from sklearn.preprocessing import [2]
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('scaler', [2]()),
    ('classifier', LogisticRegression())
])
scores = [1](pipeline, X, y, cv=5)

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Across_val_score

Btrain_test_split

CStandardScaler

DMinMaxScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing train_test_split with cross_val_score.

Using MinMaxScaler instead of StandardScaler when standardization is needed.

The pipeline uses StandardScaler to scale data, and cross_val_score to evaluate the model with cross-validation.

5fill in blank

hard

Fill all three blanks to create a pipeline that imputes missing values, scales features, and fits a classifier.

ML Python

from sklearn.impute import [1]
from sklearn.preprocessing import [2]
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('imputer', [1]()),
    ('scaler', [2]()),
    ('classifier', LogisticRegression())
])

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ASimpleImputer

BStandardScaler

CMinMaxScaler

DKNNImputer

Attempts:

3 left

💡 Hint

Common Mistakes

Using KNNImputer without importing it properly.

Mixing up MinMaxScaler and StandardScaler in this context.

The pipeline first imputes missing values using SimpleImputer, then scales features with StandardScaler, and finally fits a logistic regression model.