Polynomial regression pipeline in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class for polynomial features.

ML Python

from sklearn.preprocessing import [1]

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AStandardScaler

BLinearRegression

CPolynomialFeatures

Dtrain_test_split

Attempts:

3 left

💡 Hint

Common Mistakes

Importing LinearRegression instead of PolynomialFeatures

Importing train_test_split which is for splitting data

Importing StandardScaler which is for scaling features

The PolynomialFeatures class is used to generate polynomial and interaction features from the original features.

2fill in blank

medium

Complete the code to create a polynomial features transformer of degree 3.

ML Python

poly = PolynomialFeatures(degree=[1])

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A3

B2

C1

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 1 which does not add polynomial features

Using degree 2 which adds only quadratic terms

Using degree 4 which is higher than requested

Setting degree=3 creates polynomial features up to cubic terms.

3fill in blank

hard

Fix the error in the code to fit the polynomial regression model.

ML Python

model = LinearRegression()
X_poly = poly.[1](X)
model.fit(X_poly, y)

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Afit

Btransform

Cfit_transform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform instead of transform causing double fitting

Using fit which returns the transformer itself, not transformed data

Using predict which is for making predictions

We first fit the poly transformer separately, then use transform to convert X to polynomial features before fitting the model.

4fill in blank

hard

Fill both blanks to create a pipeline that first transforms features then fits a linear regression.

ML Python

from sklearn.pipeline import Pipeline
pipeline = Pipeline([
    ('poly', [1](degree=2)),
    ('linear', [2]())
])

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APolynomialFeatures

BLinearRegression

CStandardScaler

DLogisticRegression

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler instead of PolynomialFeatures for the first step

Using LogisticRegression which is for classification

Swapping the order of steps

The pipeline first applies PolynomialFeatures to create polynomial terms, then fits a LinearRegression model.

5fill in blank

hard

Fill all three blanks to train the pipeline and predict on test data.

ML Python

pipeline.fit([1], [2])
predictions = pipeline.[3](X_test)

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AX_train

By_train

Cpredict

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using y_train as first argument to fit

Using fit instead of predict for predictions

Using X_test in fit instead of training data

We fit the pipeline on training features and targets, then use predict to get predictions on test features.