Recursive feature elimination in ML Python - Model Pipeline Trace

Model Pipeline - Recursive feature elimination

Recursive feature elimination (RFE) is a process that helps find the most important features in data by repeatedly training a model, removing the least important feature, and retraining until the best features remain.

Data Flow - 5 Stages

1Initial data

1000 rows x 10 columns→Start with all features→1000 rows x 10 columns

Features: Age, Height, Weight, Income, Education, Gender, Hours_Worked, City, Marital_Status, Credit_Score

↓

2Train model with all features

1000 rows x 10 columns→Train model and calculate feature importance→Model trained with 10 features

Model assigns importance scores to each feature

↓

3Remove least important feature

1000 rows x 10 columns→Drop feature with lowest importance→1000 rows x 9 columns

Removed 'City' feature

↓

4Retrain model

1000 rows x 9 columns→Train model again with remaining features→Model trained with 9 features

Model recalculates feature importance

↓

5Repeat elimination

1000 rows x 9 columns→Repeat training and removing least important feature until desired number of features left→1000 rows x 5 columns

Final features: Age, Income, Education, Credit_Score, Hours_Worked

Training Trace - Epoch by Epoch

Loss
0.45 |*       
0.43 | *      
0.41 |  *     
0.40 |   *    
0.39 |    *   
      --------
      Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.72	Initial training with all 10 features
2	0.43	0.74	After removing 1 least important feature
3	0.41	0.76	After removing 2 least important features
4	0.4	0.77	After removing 3 least important features
5	0.39	0.78	After removing 4 least important features

Prediction Trace - 2 Layers

Layer 1: Input features

Layer 2: Model prediction

Model Quiz - 3 Questions

What is the main goal of recursive feature elimination?

A To find the most important features by removing the least important ones step-by-step

B To add new features to improve model accuracy

C To randomly select features for training

D To increase the number of features in the dataset

Key Insight

Recursive feature elimination helps simplify models by keeping only the most useful features, which can improve model performance and reduce complexity.

Practice

1. What is the main goal of Recursive Feature Elimination (RFE) in machine learning?

easy

A. To select the most important features by removing less important ones step by step

B. To increase the number of features in the dataset

C. To randomly shuffle the features before training

D. To create new features by combining existing ones

Solution

1. Step 1: Understand the purpose of RFE

   RFE works by removing less important features one at a time to keep only the best ones.

2. Step 2: Compare options to the purpose

   Only To select the most important features by removing less important ones step by step describes this step-by-step removal of less important features.

3. Final Answer:

   To select the most important features by removing less important ones step by step -> Option A

4. Quick Check:

   RFE = Stepwise feature removal [OK]

Hint: RFE removes features stepwise to keep the best ones [OK]

Common Mistakes:

• Thinking RFE adds or creates features
• Confusing RFE with random feature shuffling
• Believing RFE increases feature count

2. Which of the following is the correct way to import Recursive Feature Elimination from scikit-learn in Python?

easy

A. from sklearn.feature_selection import RecursiveFeatureElimination

B. from sklearn.feature_selection import RFE

C. import sklearn.feature_selection.RFE as rfe

D. from sklearn.selection import RFE

Solution

1. Step 1: Recall the correct import statement

   The class is named RFE and is in sklearn.feature_selection.

2. Step 2: Match options with correct syntax

   from sklearn.feature_selection import RFE correctly imports RFE from sklearn.feature_selection.

3. Final Answer:

   from sklearn.feature_selection import RFE -> Option B

4. Quick Check:

   Correct import is 'from sklearn.feature_selection import RFE' [OK]

Hint: Remember: RFE is imported directly from sklearn.feature_selection [OK]

Common Mistakes:

• Using wrong module name like sklearn.selection
• Trying to import full name RecursiveFeatureElimination
• Using incorrect import syntax

3. Given the following Python code using RFE, what will be the output of print(selected_features)?

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

iris = load_iris()
X, y = iris.data, iris.target
model = LogisticRegression(max_iter=200)
rfe = RFE(model, n_features_to_select=2)
rfe.fit(X, y)
selected_features = rfe.support_
print(selected_features)

medium

A. [ True True False False ]

B. [False True False True ]

C. [ True False True False ]

D. [False False True True ]

Solution

1. Step 1: Understand RFE output support_

   The support_ attribute is a boolean array showing which features are selected.

2. Step 2: Run RFE with LogisticRegression on iris dataset

   RFE selects the two most important features, which for iris are the last two features (petal length and petal width), so the output is [False False True True].

3. Final Answer:

   [False False True True ] -> Option D

4. Quick Check:

   RFE selects last two iris features = [False False True True] [OK]

Hint: Iris important features are last two; RFE selects those [OK]

Common Mistakes:

• Assuming first two features are selected
• Confusing support_ with ranking_
• Not setting max_iter causing convergence warnings

4. Identify the error in this RFE usage code and choose the correct fix:

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

model = LogisticRegression()
rfe = RFE(model, n_features_to_select=0)
rfe.fit(X, y)

medium

A. n_features_to_select cannot be zero; set it to a positive integer

B. LogisticRegression must be imported from sklearn.linear_model.linear_model

C. RFE requires a random_state parameter

D. fit method requires sample_weight argument

Solution

1. Step 1: Check parameter n_features_to_select

   This parameter must be at least 1 or None, zero is invalid.

2. Step 2: Identify correct fix

   Setting n_features_to_select to a positive integer fixes the error.

3. Final Answer:

   n_features_to_select cannot be zero; set it to a positive integer -> Option A

4. Quick Check:

   n_features_to_select > 0 required [OK]

Hint: n_features_to_select must be positive, never zero [OK]

Common Mistakes:

• Setting n_features_to_select to zero
• Wrong import paths for LogisticRegression
• Thinking random_state is mandatory for RFE

5. You have a dataset with 20 features and want to use RFE with a Random Forest model to select the top 5 features. Which of the following code snippets correctly applies RFE and outputs the names of the selected features assuming your data is in a pandas DataFrame df and target in y?

hard

A. from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
rfe = RFE(model, n_features_to_select=5)
rfe.fit(df, y)
selected = df.columns[rfe.ranking_ <= 5]
print(selected.tolist())

B. from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
rfe = RFE(model, n_features_to_select=5)
rfe.fit(y, df)
selected = df.columns[rfe.support_]
print(selected.tolist())

C. from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
rfe = RFE(model, n_features_to_select=5)
rfe.fit(df, y)
selected = df.columns[rfe.support_]
print(selected.tolist())

D. from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
rfe = RFE(model, n_features_to_select=5)
rfe.fit(df, y)
selected = df.columns[rfe.ranking_ == 5]
print(selected.tolist())

Solution

1. Step 1: Check correct fit method usage

   Features (df) must be first argument, target (y) second in fit.

2. Step 2: Select features using support_ boolean mask

   Use rfe.support_ to get selected features, then map to column names.

3. Final Answer:

   Code snippet A correctly fits and selects features using support_ mask -> Option C

4. Quick Check:

   fit(df, y) + support_ mask = correct feature selection [OK]

Hint: fit(df, y) and use support_ to get selected features [OK]

Common Mistakes:

• Swapping X and y in fit method
• Using ranking_ == 5 instead of support_
• Not converting boolean mask to column names