Polynomial features in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class for creating polynomial features.

ML Python

from sklearn.preprocessing import [1]

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AStandardScaler

BMinMaxScaler

CLinearRegression

DPolynomialFeatures

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a scaler class instead of PolynomialFeatures.

Confusing PolynomialFeatures with regression models.

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

2fill in blank

medium

Complete the code to create polynomial features of degree 3.

ML Python

poly = PolynomialFeatures(degree=[1])

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A3

B1

C2

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 1 which does not add polynomial features.

Choosing degree 2 when cubic features are needed.

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

3fill in blank

hard

Fix the error in the code to transform features using polynomial features.

ML Python

X_poly = poly.[1](X)

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Apredict

Btransform

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit which does not transform data.

Using transform without fitting first.

The fit_transform method fits the transformer and applies the transformation in one step.

4fill in blank

hard

Fill both blanks to create polynomial features and exclude the bias term.

ML Python

poly = PolynomialFeatures(degree=[1], include_bias=[2])

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A3

BTrue

CFalse

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to exclude the bias term when not needed.

Using the wrong degree for the polynomial features.

Degree 3 creates cubic features. Setting include_bias=False excludes the constant term.

5fill in blank

hard

Fill all three blanks to create polynomial features, transform data, and print the shape of the result.

ML Python

poly = PolynomialFeatures(degree=[1], include_bias=[2])
X_poly = poly.[3](X)

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A2

BFalse

Cfit_transform

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Including bias when not needed.

Using transform without fitting first.

Degree 2 creates quadratic features. Bias is excluded with False. fit_transform fits and transforms the data.