Feature union in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the class used to combine features.

ML Python

from sklearn.pipeline import [1]

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

AStandardScaler

BPCA

CFeatureUnion

DGridSearchCV

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated classes like StandardScaler or PCA.

Confusing FeatureUnion with Pipeline.

FeatureUnion is the class used to combine multiple feature extraction processes into one.

2fill in blank

medium

Complete the code to create a FeatureUnion combining two transformers.

ML Python

union = FeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', [1]())])

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

AStandardScaler

BMinMaxScaler

COneHotEncoder

DPolynomialFeatures

Attempts:

3 left

💡 Hint

Common Mistakes

Using OneHotEncoder which is for categorical data encoding.

Using PolynomialFeatures which creates new features.

StandardScaler is commonly used to scale features before combining them.

3fill in blank

hard

Fix the error in the code to correctly fit the FeatureUnion on data X.

ML Python

union = FeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', StandardScaler())])
union.[1](X)

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

Apredict

Bfit_transform

Ctransform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform() before fitting causes errors.

Using predict() which is for models, not transformers.

To prepare the FeatureUnion for transformation, we first call fit on the data.

4fill in blank

hard

Fill both blanks to create a pipeline that first applies FeatureUnion then fits a classifier.

ML Python

pipeline = Pipeline([('features', [1]), ('clf', [2])])

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

AFeatureUnion(transformer_list=[('pca', PCA(n_components=2)), ('scaler', StandardScaler())])

BLogisticRegression()

CRandomForestClassifier()

DStandardScaler()

Attempts:

3 left

💡 Hint

Common Mistakes

Using StandardScaler as the classifier step.

Using RandomForestClassifier when LogisticRegression is expected.

FeatureUnion combines features, then LogisticRegression fits the combined features.

5fill in blank

hard

Fill all three blanks to create a FeatureUnion with two transformers and fit_transform on data X.

ML Python

union = FeatureUnion(transformer_list=[('pca', [1]), ('scaler', [2])])
X_transformed = union.[3](X)

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

APCA(n_components=3)

BStandardScaler()

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of fit_transform for transformation.

Mixing up transformer instances with method names.

PCA and StandardScaler are transformers; fit_transform fits and transforms the data.

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