Model interpretability (SHAP, LIME) in ML Python - Model Pipeline Trace

Model Pipeline - Model interpretability (SHAP, LIME)

This pipeline shows how a machine learning model is trained and then explained using SHAP and LIME methods. These methods help us understand why the model makes certain predictions by showing the importance of each feature.

Data Flow - 6 Stages

1Raw Data Input

1000 rows x 5 columns→Collect dataset with 5 features and target label→1000 rows x 6 columns

Feature1=5.1, Feature2=3.5, Feature3=1.4, Feature4=0.2, Feature5=0.5, Label=0

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2Data Preprocessing

1000 rows x 6 columns→Fill missing values and scale features→1000 rows x 6 columns

Feature1=0.52, Feature2=0.68, Feature3=0.15, Feature4=0.05, Feature5=0.10, Label=0

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3Train/Test Split

1000 rows x 6 columns→Split data into 80% train and 20% test sets→Train: 800 rows x 6 columns, Test: 200 rows x 6 columns

Train sample: Feature1=0.52, Label=0; Test sample: Feature1=0.60, Label=1

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4Model Training

800 rows x 5 columns→Train a Random Forest classifier→Trained model

Model learns patterns to predict label from features

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5Model Explanation (SHAP)

Test sample features (1 row x 5 columns)→Calculate SHAP values for each feature→SHAP values vector (1 row x 5 features)

SHAP values: Feature1=0.2, Feature2=-0.1, Feature3=0.05, Feature4=0.0, Feature5=0.1

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6Model Explanation (LIME)

Test sample features (1 row x 5 columns)→Generate local linear explanation→LIME feature importance scores (1 row x 5 features)

LIME scores: Feature1=0.18, Feature2=-0.12, Feature3=0.07, Feature4=0.01, Feature5=0.09

Training Trace - Epoch by Epoch

Loss
0.65 |*****
0.50 |****
0.40 |***
0.35 |**
0.33 |*
     +------------
      1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate accuracy
2	0.50	0.75	Loss decreases and accuracy improves
3	0.40	0.82	Model continues to learn well
4	0.35	0.85	Training converges with good accuracy
5	0.33	0.87	Final epoch with stable loss and accuracy

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Random Forest Prediction

Layer 3: SHAP Explanation

Layer 4: LIME Explanation

Model Quiz - 3 Questions

What does a positive SHAP value for a feature indicate?

A The feature pushes the prediction away from the predicted class

B The feature pushes the prediction towards the predicted class

C The feature has no effect on the prediction

D The feature is missing in the data

Key Insight

Using SHAP and LIME helps us see which features influence the model's decisions. This builds trust and helps us understand the model better, especially when it makes predictions on new data.