Feature union in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Feature Union in machine learning?

Feature Union is a technique that combines multiple feature extraction processes into one. It joins different sets of features side by side to create a bigger feature set for a model.

beginner

Why use Feature Union instead of just one feature extractor?

Using Feature Union lets you combine different types of features that capture different information. This can help the model learn better by having more diverse data.

intermediate

How does Feature Union work internally?

Feature Union runs each feature extractor separately on the input data, then stacks their outputs horizontally (side by side) to form one combined feature matrix.

intermediate

Give a simple example of using Feature Union in Python with scikit-learn.

You can combine a text vectorizer and a numeric scaler like this: FeatureUnion([('text', CountVectorizer()), ('num', StandardScaler())]). This creates features from text and numbers together.

intermediate

What is the difference between Feature Union and Pipeline in scikit-learn?

Feature Union combines features from parallel transformers side by side. Pipeline applies transformers sequentially, one after another, transforming data step by step.

What does Feature Union do with the outputs of multiple feature extractors?

A Stacks them side by side to create a combined feature set

B Adds them together element-wise

C Selects the best output and discards others

D Runs them one after another in sequence

Feature Union stacks outputs horizontally, combining features side by side.

Which scikit-learn class is used to perform Feature Union?

A FeatureUnion

B Pipeline

C GridSearchCV

D StandardScaler

FeatureUnion is the class designed to combine multiple feature extractors.

Feature Union is most useful when:

A You want to apply transformations sequentially

B You want to reduce the number of features

C You want to combine different types of features from the same data

D You want to train multiple models separately

Feature Union helps combine different feature sets extracted in parallel.

What is the main difference between Feature Union and Pipeline?

A Feature Union is for model training; Pipeline is for data cleaning

B Feature Union applies steps sequentially; Pipeline combines features side by side

C Feature Union is only for numeric data; Pipeline is only for text data

D Feature Union combines features side by side; Pipeline applies steps one after another

Feature Union merges features horizontally; Pipeline chains transformations vertically.

If you want to combine text features and numeric features for a model, which technique is best?

A Grid Search

B Feature Union

C Cross Validation

D Principal Component Analysis

Feature Union allows combining different feature types into one feature set.

Explain in your own words what Feature Union is and why it is useful in machine learning.

Think about how combining different views of data can help a model.

Describe the difference between Feature Union and Pipeline in scikit-learn.

Consider how data flows through each.

Practice

1. What is the main purpose of using FeatureUnion in machine learning?

easy

A. To combine multiple feature extraction methods into a single feature set

B. To split data into training and testing sets

C. To reduce the number of features by selecting the best ones

D. To train multiple models and average their predictions

Solution

1. Step 1: Understand FeatureUnion's role

   FeatureUnion is used to combine different feature extraction methods so their outputs join into one feature set.

2. Step 2: Compare with other options

   Splitting data, feature selection, and model averaging are different tasks not done by FeatureUnion.

3. Final Answer:

   To combine multiple feature extraction methods into a single feature set -> Option A

4. Quick Check:

   FeatureUnion = Combine features [OK]

Hint: FeatureUnion joins features, not data splits or models [OK]

Common Mistakes:

• Confusing FeatureUnion with data splitting
• Thinking it selects features instead of combining
• Mixing it up with model ensemble methods

2. Which of the following is the correct way to create a FeatureUnion with two transformers named 'tf1' and 'tf2'?

easy

A. FeatureUnion(tf1=transformer1, tf2=transformer2)

B. FeatureUnion({'tf1': transformer1, 'tf2': transformer2})

C. FeatureUnion([('tf1', transformer1), ('tf2', transformer2)])

D. FeatureUnion(transformer1, transformer2)

Solution

1. Step 1: Recall FeatureUnion syntax

   FeatureUnion expects a list of tuples, each tuple with a name and a transformer.

2. Step 2: Check each option

   FeatureUnion([('tf1', transformer1), ('tf2', transformer2)]) uses a list of tuples correctly. Options B, C, and D use wrong data structures or missing list.

3. Final Answer:

   FeatureUnion([('tf1', transformer1), ('tf2', transformer2)]) -> Option C

4. Quick Check:

   FeatureUnion needs list of (name, transformer) tuples [OK]

Hint: Use list of (name, transformer) tuples for FeatureUnion [OK]

Common Mistakes:

• Passing a dictionary instead of list of tuples
• Passing transformers without names
• Passing transformers as separate arguments

3. Given the code below, what will be the shape of X_transformed?

from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import numpy as np

X = np.array([[1, 2, 3], [4, 5, 6]])

union = FeatureUnion([
    ('scale', StandardScaler()),
    ('pca', PCA(n_components=1))
])

X_transformed = union.fit_transform(X)

medium

A. (2, 1)

B. (2, 3)

C. (2, 2)

D. (2, 4)

Solution

1. Step 1: Analyze each transformer output

   StandardScaler keeps original shape (2 samples, 3 features) so output shape is (2,3). PCA with n_components=1 outputs (2,1).

2. Step 2: Combine outputs with FeatureUnion

   FeatureUnion concatenates outputs horizontally: (2,3) + (2,1) = (2,4).

3. Final Answer:

   (2, 4) -> Option D

4. Quick Check:

   Concatenate (2,3) and (2,1) = (2,4) [OK]

Hint: FeatureUnion concatenates horizontally, sum feature counts [OK]

Common Mistakes:

• Assuming PCA output replaces original features
• Thinking FeatureUnion stacks vertically
• Ignoring output shapes of individual transformers

4. You wrote this code but get an error:

union = FeatureUnion([
    ('scale', StandardScaler()),
    ('pca', PCA(n_components=3))
])

X_transformed = union.fit_transform([[1, 2], [3, 4], [5, 6]])

What is the likely cause of the error?

medium

A. PCA cannot have n_components greater than input features

B. StandardScaler requires 3D input, but input is 2D

C. FeatureUnion requires transformers to have fit_predict method

D. Input data must be a pandas DataFrame, not a list

Solution

1. Step 1: Check input data shape

   The input X = [[1,2],[3,4],[5,6]] has shape (3, 2), meaning 2 features.

2. Step 2: Analyze PCA configuration

   PCA(n_components=3) requests 3 components, but only 2 features are available, causing a ValueError.

3. Final Answer:

   PCA cannot have n_components greater than input features -> Option A

4. Quick Check:

   PCA n_components ≤ features [OK]

Hint: Check PCA n_components ≤ number of features [OK]

Common Mistakes:

• Assuming StandardScaler needs 3D input
• Thinking FeatureUnion needs fit_predict
• Believing input must be DataFrame

5. You want to combine text and numeric features for a model. You have a TfidfVectorizer for text and StandardScaler for numeric data. How do you use FeatureUnion to prepare the data correctly?

hard

A. Apply TfidfVectorizer and StandardScaler separately, then add their outputs manually

B. Use FeatureUnion with transformers for text and numeric, each applied to their columns via ColumnTransformer

C. Use FeatureUnion directly on raw data without preprocessing

D. Use StandardScaler on text data and TfidfVectorizer on numeric data

Solution

1. Step 1: Understand data types and transformers

   Text and numeric data need different preprocessing. TfidfVectorizer works on text, StandardScaler on numeric features.

2. Step 2: Use ColumnTransformer with FeatureUnion

   Apply each transformer to correct columns using ColumnTransformer, then combine with FeatureUnion to merge features.

3. Final Answer:

   Use FeatureUnion with transformers for text and numeric, each applied to their columns via ColumnTransformer -> Option B

4. Quick Check:

   Separate preprocessing per data type, then combine [OK]

Hint: Preprocess each data type separately, then combine features [OK]

Common Mistakes:

• Applying wrong transformer to wrong data type
• Skipping column selection before FeatureUnion
• Trying to combine raw data without preprocessing