Mutual information for feature selection in ML Python - Model Pipeline Trace

Model Pipeline - Mutual information for feature selection

This pipeline uses mutual information to find which features are most helpful to predict the target. It selects the best features before training a simple model to improve accuracy and reduce noise.

Data Flow - 5 Stages

1Raw data input

1000 rows x 10 columns→Load dataset with 10 features and 1 target column→1000 rows x 10 columns

Feature1=5.1, Feature2=3.5, ..., Feature10=0.2, Target=1

↓

2Calculate mutual information

1000 rows x 10 columns→Compute mutual information score between each feature and target→10 scores (one per feature)

Feature1=0.15, Feature2=0.05, ..., Feature10=0.12

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3Select top features

1000 rows x 10 columns→Pick top 3 features with highest mutual information scores→1000 rows x 3 columns

Selected features: Feature1, Feature5, Feature10

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4Train model

1000 rows x 3 columns→Train logistic regression model using selected features→Trained model

Model trained on Feature1, Feature5, Feature10

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5Evaluate model

Test set 200 rows x 3 columns→Calculate accuracy and loss on test data→Accuracy=0.85, Loss=0.35

Test accuracy 85%, loss 0.35

Training Trace - Epoch by Epoch

Loss: 0.65 |*****     
      0.50 |*******   
      0.42 |********  
      0.38 |********* 
      0.35 |*********

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate loss and accuracy
2	0.50	0.72	Loss decreases and accuracy improves
3	0.42	0.78	Model continues to improve
4	0.38	0.82	Loss decreases further, accuracy rises
5	0.35	0.85	Training converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input selected features

Layer 2: Linear combination

Layer 3: Sigmoid activation

Layer 4: Threshold decision

Model Quiz - 3 Questions

What does mutual information measure in this pipeline?

A How much each feature tells about the target

B The average value of each feature

C The number of missing values in features

D The time taken to train the model

Key Insight

Mutual information helps pick features that share the most information with the target. Using these features improves model learning speed and accuracy by focusing on the most relevant data.