Polynomial features in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

from sklearn.preprocessing import [1]

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APolynomialFeatures

BStandardScaler

CMinMaxScaler

DOneHotEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Importing scalers instead of polynomial features.

Confusing one-hot encoding with polynomial 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 polynomial features of degree 3.

Data Analysis Python

poly = PolynomialFeatures(degree=[1])

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A2

B3

C1

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using degree 1 which does not add polynomial features.

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 transform the data using polynomial features.

Data Analysis Python

X_poly = poly.[1](X)

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Afit

Btransform

Cfit_transform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using only fit which does not transform data.

Using transform without fitting first.

Using predict which is not a method here.

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 without the bias term and degree 2.

Data Analysis Python

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

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A2

BTrue

CFalse

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Including bias when not needed.

Using wrong degree value.

Degree 2 creates quadratic features. Setting include_bias=False removes the constant term.

5fill in blank

hard

Fill all three blanks to create polynomial features of degree 2, exclude bias, and transform data X.

Data Analysis 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

Using degree 3 instead of 2.

Including bias when not needed.

Using only fit or transform instead of fit_transform.

Degree 2 creates quadratic features, bias is excluded, and fit_transform applies the transformation.