Creating interaction features in ML Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new feature by multiplying two existing features.

ML Python

df['interaction'] = df['feature1'] [1] df['feature2']

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

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using division which may cause errors if denominator is zero.

Multiplying two features creates an interaction feature that captures combined effects.

2fill in blank

medium

Complete the code to create interaction features for all pairs of columns in the DataFrame.

ML Python

from itertools import combinations

for col1, col2 in combinations(df.columns, 2):
    df[f'{col1}_x_{col2}'] = df[col1] [1] df[col2]

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

A+

B*

C/

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition which does not capture interaction.

Using division which can cause errors.

Multiplying pairs of features creates interaction terms that capture combined effects.

3fill in blank

hard

Fix the error in the code to correctly create an interaction feature between 'age' and 'income'.

ML Python

df['age_income_interaction'] = df['age'] [1] df['income']

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

A+

B/

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using division which may cause errors.

The interaction feature should multiply 'age' and 'income' to capture their combined effect.

4fill in blank

hard

Fill both blanks to create a dictionary of interaction features for words longer than 3 characters.

ML Python

interaction_dict = {word: len(word) [1] 2 for word in words if len(word) [2] 3}

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

A**

B%

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using modulus '%' instead of power '**'.

Using '<' instead of '>' for filtering.

The code squares the length of words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary of interaction features where keys are uppercase words and values are their lengths if length is greater than 4.

ML Python

interaction_features = [1]: [2] for word in words if len(word) [3] 4}

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

Aword.upper()

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Using word instead of word.upper() for keys.

The dictionary maps uppercase words to their lengths for words longer than 4 characters.

Practice

1. What is the main purpose of creating interaction features in machine learning?

easy

A. To capture the combined effect of two or more features on the target

B. To reduce the number of features in the dataset

C. To normalize the features to a common scale

D. To remove irrelevant features automatically

Solution

1. Step 1: Understand interaction features

   Interaction features combine two or more features to capture their joint effect on the target variable.

2. Step 2: Compare options

   Only To capture the combined effect of two or more features on the target describes capturing combined effects, which is the purpose of interaction features.

3. Final Answer:

   To capture the combined effect of two or more features on the target -> Option A

4. Quick Check:

   Interaction features = combined effect [OK]

Hint: Interaction features capture combined effects of features [OK]

Common Mistakes:

• Confusing interaction features with feature scaling
• Thinking interaction features reduce feature count
• Assuming interaction features remove irrelevant features

2. Which of the following is the correct way to create an interaction feature between two numeric features x1 and x2 in Python?

easy

A. interaction = x1 * x2

B. interaction = x1 - x2

C. interaction = x1 / x2

D. interaction = x1 + x2

Solution

1. Step 1: Recall how interaction features are created

   Interaction features are typically created by multiplying numeric features to capture their joint effect.

2. Step 2: Check each option

   Only multiplication (x1 * x2) correctly creates an interaction feature.

3. Final Answer:

   interaction = x1 * x2 -> Option A

4. Quick Check:

   Interaction = multiply features [OK]

Hint: Multiply numeric features to create interaction features [OK]

Common Mistakes:

• Using addition instead of multiplication
• Using division or subtraction which do not capture interaction
• Confusing interaction with feature scaling

3. Given the code below, what will be the output of print(df['interaction'].tolist())?

import pandas as pd

df = pd.DataFrame({'x1': [1, 2, 3], 'x2': [4, 5, 6]})
df['interaction'] = df['x1'] * df['x2']
print(df['interaction'].tolist())

medium

A. [4, 5, 6]

B. [5, 7, 9]

C. [1, 2, 3]

D. [4, 10, 18]

Solution

1. Step 1: Calculate interaction feature values

   Multiply each pair: 1*4=4, 2*5=10, 3*6=18.

2. Step 2: Verify output list

   The list of interaction values is [4, 10, 18].

3. Final Answer:

   [4, 10, 18] -> Option D

4. Quick Check:

   Multiplying pairs = [4, 10, 18] [OK]

Hint: Multiply row-wise values for interaction feature list [OK]

Common Mistakes:

• Adding instead of multiplying features
• Confusing original features with interaction
• Misreading the DataFrame values

4. The following code attempts to create an interaction feature between two categorical features color and shape. What is the error?

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue'], 'shape': ['circle', 'square']})
df['interaction'] = df['color'] * df['shape']
print(df['interaction'])

medium

A. DataFrame columns must be numeric to create interaction

B. The DataFrame is missing a target column

C. You cannot multiply string columns directly; need encoding first

D. The print statement syntax is incorrect

Solution

1. Step 1: Understand data types for interaction

   Multiplying string columns causes an error because strings cannot be multiplied directly.

2. Step 2: Identify correct approach

   Categorical features must be encoded (e.g., one-hot or label encoding) before creating interaction features.

3. Final Answer:

   You cannot multiply string columns directly; need encoding first -> Option C

4. Quick Check:

   Multiply strings error = need encoding [OK]

Hint: Encode categorical features before multiplying [OK]

Common Mistakes:

• Trying to multiply raw string columns
• Ignoring data type requirements for interaction
• Assuming print syntax is wrong

5. You have two categorical features: Gender with values ['Male', 'Female'] and Smoker with values ['Yes', 'No']. How would you create an interaction feature to help a model learn their combined effect?

hard

A. Multiply the raw string columns directly

B. One-hot encode both features, then multiply corresponding columns

C. Add the string values together as new strings

D. Ignore interaction features for categorical data

Solution

1. Step 1: Encode categorical features

   Convert 'Gender' and 'Smoker' into one-hot encoded numeric columns.

2. Step 2: Create interaction features

   Multiply corresponding one-hot columns (e.g., Male*Yes) to capture combined effect.

3. Final Answer:

   One-hot encode both features, then multiply corresponding columns -> Option B

4. Quick Check:

   Encode then multiply categorical features [OK]

Hint: One-hot encode then multiply for categorical interaction [OK]

Common Mistakes:

• Trying to multiply raw strings
• Concatenating strings instead of encoding
• Skipping interaction features for categorical data