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]

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

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)

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

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.

Practice

1. What is the main purpose of using Polynomial features in data analysis?

easy

A. To reduce the number of features in the dataset

B. To capture non-linear relationships by adding powers and combinations of features

C. To remove outliers from the data

D. To convert categorical data into numerical data

Solution

1. Step 1: Understand what polynomial features do

   Polynomial features create new features by raising existing features to powers and combining them, which helps model curves.

2. Step 2: Identify the purpose in modeling

   This helps capture non-linear relationships that simple linear features cannot represent.

3. Final Answer:

   To capture non-linear relationships by adding powers and combinations of features -> Option B

4. Quick Check:

   Polynomial features = capture curves [OK]

Hint: Polynomial features add powers to model curves, not reduce features [OK]

Common Mistakes:

• Thinking polynomial features reduce feature count
• Confusing polynomial features with data cleaning
• Assuming polynomial features handle categorical data

2. Which of the following is the correct way to create polynomial features of degree 2 using scikit-learn in Python?

easy

A. from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(1)
X_poly = poly.transform(X)

B. from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit(X)

C. from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=3)
X_poly = poly.fit_transform(X)

D. from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

Solution

1. Step 1: Import and initialize PolynomialFeatures correctly

   Use degree=2 to specify the polynomial degree.

2. Step 2: Use fit_transform to fit and transform the data in one step

   fit_transform returns the transformed feature matrix, which is needed for modeling.

3. Final Answer:

   from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2) X_poly = poly.fit_transform(X) -> Option D

4. Quick Check:

   Use degree=2 and fit_transform() [OK]

Hint: Use fit_transform() with degree=2 to create polynomial features [OK]

Common Mistakes:

• Using transform() without fitting first
• Setting wrong degree value
• Calling fit() without transform or fit_transform()

3. Given the code below, what is the shape of X_poly?

import numpy as np
from sklearn.preprocessing import PolynomialFeatures
X = np.array([[2, 3]])
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

medium

A. (1, 6)

B. (1, 3)

C. (2, 3)

D. (2, 6)

Solution

1. Step 1: Count original features and polynomial features

   Original X has 2 features. Degree 2 polynomial features include: - bias (1) - original features (2) - squared features and interaction (3): x1^2, x1*x2, x2^2 Total = 1 + 2 + 3 = 6 features.

2. Step 2: Check number of samples

   X has 1 sample (row), so output shape is (1, 6).

3. Final Answer:

   (1, 6) -> Option A

4. Quick Check:

   Features after degree 2 = 6 [OK]

Hint: Degree 2 with 2 features gives 6 output features [OK]

Common Mistakes:

• Ignoring bias (constant) term
• Counting only squared terms, missing interactions
• Confusing samples with features in shape

4. Identify the error in the following code that tries to create polynomial features of degree 3:

from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(3)
X_poly = poly.fit_transform([[1, 2], [3, 4]])

medium

A. PolynomialFeatures requires degree to be specified as a keyword argument

B. fit_transform cannot be used on lists, input must be a numpy array

C. No error, the code runs correctly

D. PolynomialFeatures does not support degree 3

Solution

1. Step 1: Check PolynomialFeatures initialization

   Degree can be passed as positional argument, so PolynomialFeatures(3) is valid.

2. Step 2: Check input type for fit_transform

   fit_transform accepts array-like inputs, including lists, so [[1, 2], [3, 4]] is valid.

3. Final Answer:

   No error, the code runs correctly -> Option C

4. Quick Check:

   Degree 3 and list input are valid [OK]

Hint: PolynomialFeatures accepts degree as positional and list input [OK]

Common Mistakes:

• Thinking degree must be keyword argument
• Assuming lists are invalid input
• Believing degree 3 is unsupported

5. You have a dataset with 3 features and want to add polynomial features up to degree 2 but exclude the bias (constant) term. Which code snippet correctly achieves this?

hard

A. poly = PolynomialFeatures(degree=2, include_bias=False)
X_poly = poly.fit_transform(X)

B. poly = PolynomialFeatures(degree=2, include_bias=True)
X_poly = poly.fit_transform(X)
X_poly = X_poly[:, :1]

C. poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
X_poly = X_poly[:, :1]

D. poly = PolynomialFeatures(degree=2, bias=False)
X_poly = poly.fit_transform(X)

Solution

1. Step 1: Understand the include_bias parameter

   Setting include_bias=False excludes the constant (bias) term from the output features.

2. Step 2: Use correct parameter and method

   poly = PolynomialFeatures(degree=2, include_bias=False) and then fit_transform(X) correctly creates polynomial features without bias.

3. Final Answer:

   poly = PolynomialFeatures(degree=2, include_bias=False) X_poly = poly.fit_transform(X) -> Option A

4. Quick Check:

   include_bias=False excludes constant term [OK]

Hint: Use include_bias=False to skip constant term [OK]

Common Mistakes:

• Trying to remove bias by slicing output array
• Using wrong parameter name like bias=False
• Assuming include_bias defaults to False