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.