Feature selection methods in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of feature selection in machine learning?

The main goal of feature selection is to choose the most important features from the data to improve model performance, reduce overfitting, and make the model simpler and faster.

beginner

Name three common types of feature selection methods.

The three common types are: 1) Filter methods, 2) Wrapper methods, and 3) Embedded methods.

intermediate

How do filter methods select features?

Filter methods select features based on their statistical relationship with the target variable, such as correlation or mutual information, without involving any machine learning model.

intermediate

What is the difference between wrapper and embedded methods?

Wrapper methods use a machine learning model to evaluate feature subsets by training and testing repeatedly, while embedded methods perform feature selection during the model training process itself.

beginner

Why can feature selection help reduce overfitting?

Feature selection removes irrelevant or noisy features, which reduces the chance that the model learns patterns from noise, thus helping the model generalize better to new data.

Which feature selection method evaluates features using a model repeatedly?

A Wrapper methods

B Filter methods

C Embedded methods

D Dimensionality reduction

Wrapper methods select features by training and testing a model on different feature subsets repeatedly.

Which method selects features based on correlation with the target variable?

A Filter methods

B Wrapper methods

C Embedded methods

D Clustering

Filter methods use statistical measures like correlation to select features without involving a model.

What is a key advantage of embedded feature selection methods?

A They do not require training a model

B They always select all features

C They use only statistical tests

D They select features during model training

Embedded methods perform feature selection as part of the model training process.

Feature selection helps reduce overfitting by:

A Increasing model complexity

B Adding more features

C Removing irrelevant features

D Ignoring the target variable

Removing irrelevant or noisy features helps the model avoid learning noise, reducing overfitting.

Which of these is NOT a feature selection method?

A Filter method

B Principal Component Analysis

C Embedded method

D Wrapper method

Principal Component Analysis is a dimensionality reduction technique, not a feature selection method.

Explain the differences between filter, wrapper, and embedded feature selection methods.

Think about whether the method uses a model and when feature selection happens.

Describe why feature selection is important in building machine learning models.

Consider how fewer features affect model learning and performance.

Practice

1. Which of the following best describes the purpose of feature selection in machine learning?

easy

A. To choose the most important features to improve model performance

B. To increase the number of features in the dataset

C. To randomly remove features from the dataset

D. To convert features into labels for training

Solution

1. Step 1: Understand feature selection goal

   Feature selection aims to pick the most useful features that help the model learn better.

2. Step 2: Evaluate options

   Only To choose the most important features to improve model performance correctly states that feature selection chooses important features to improve model performance.

3. Final Answer:

   To choose the most important features to improve model performance -> Option A

4. Quick Check:

   Feature selection = pick important features [OK]

Hint: Feature selection picks useful features, not random or all [OK]

Common Mistakes:

• Thinking feature selection adds features
• Confusing feature selection with feature engineering
• Believing feature selection changes labels

2. Which Python library provides the SelectKBest feature selection method?

easy

A. pandas

B. scikit-learn

C. numpy

D. matplotlib

Solution

1. Step 1: Recall common ML libraries

   Scikit-learn is the main library for machine learning tools including feature selection.

2. Step 2: Match method to library

   SelectKBest is part of scikit-learn's feature_selection module, not pandas, numpy, or matplotlib.

3. Final Answer:

   scikit-learn -> Option B

4. Quick Check:

   SelectKBest = scikit-learn [OK]

Hint: SelectKBest is from scikit-learn, not data or plotting libs [OK]

Common Mistakes:

• Choosing pandas because it handles data
• Confusing numpy with ML feature tools
• Selecting matplotlib which is for plotting

3. What will be the output shape of features after applying VarianceThreshold(threshold=0.1) on a dataset with shape (100, 5) where only 3 features have variance above 0.1?

medium

A. (5, 100)

B. (100, 5)

C. (3, 100)

D. (100, 3)

Solution

1. Step 1: Understand VarianceThreshold effect

   VarianceThreshold removes features with variance below the threshold, keeping only those above it.

2. Step 2: Apply to given data

   Since 3 features have variance above 0.1, only those 3 remain. The number of samples (100) stays the same.

3. Final Answer:

   (100, 3) -> Option D

4. Quick Check:

   VarianceThreshold keeps features with variance > threshold [OK]

Hint: Output shape keeps rows, columns = features passing threshold [OK]

Common Mistakes:

• Confusing rows and columns in shape
• Assuming all features remain
• Thinking variance threshold changes sample count

4. Consider this code snippet:

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
rfe = RFE(model, n_features_to_select=2)
rfe.fit(X, y)
selected = rfe.transform(X)
print(selected.shape)

If X has shape (50, 4), but the output shape is (50, 4), what is the likely error?

medium

A. RFE does not reduce features automatically

B. n_features_to_select is greater than number of features

C. RFE was not fitted before transform

D. LogisticRegression model is incompatible with RFE

Solution

1. Step 1: Understand RFE usage

   RFE must be fitted before calling transform to reduce features.

2. Step 2: Check given code and output

   If output shape is unchanged, likely transform was called before fitting or fitting failed.

3. Step 3: Identify cause

   Since code shows fitting before transform, but output shape unchanged, the most common cause is that transform was called on unfitted RFE or fit did not complete properly.

4. Final Answer:

   RFE was not fitted before transform -> Option C

5. Quick Check:

   Fit RFE before transform to reduce features [OK]

Hint: Ensure RFE is fitted before transform [OK]

Common Mistakes:

• Assuming transform always reduces features without fitting
• Ignoring the need to fit RFE
• Thinking model type causes shape issue

5. You have a dataset with 10 features, but 4 are highly correlated and 2 have very low variance. Which feature selection approach best improves model simplicity and speed?

hard

A. Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features

B. Use RFE with all features and keep all 10

C. Use SelectKBest to pick top 6 features by univariate scores

D. Randomly drop 4 features to reduce dimensionality

Solution

1. Step 1: Identify problem features

   Low variance features add little info; correlated features add redundancy.

2. Step 2: Choose method to remove both

   VarianceThreshold removes low variance features; correlation filter removes redundant correlated features.

3. Step 3: Evaluate options

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features combines both methods to improve simplicity and speed effectively.

4. Final Answer:

   Apply VarianceThreshold to remove low variance, then use correlation filter to drop correlated features -> Option A

5. Quick Check:

   Remove low variance + correlated features = simpler model [OK]

Hint: Combine variance and correlation filters for best feature reduction [OK]

Common Mistakes:

• Using only one method ignoring other feature issues
• Randomly dropping features without reason
• Keeping all features with RFE without reduction