Stacking and blending in ML Python - Model Pipeline Trace

Model Pipeline - Stacking and blending

Stacking and blending are ways to combine multiple simple models to make a stronger model. They use the predictions of base models as new inputs for a final model that learns to improve overall accuracy.

Data Flow - 6 Stages

1Original Data

1000 rows x 10 columns→Raw dataset with features and target→1000 rows x 10 columns

Features: age, income, score,... Target: buy or not

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2Train Base Models

800 rows x 10 columns→Train multiple base models on training data→3 trained base models

Train decision tree, logistic regression, and SVM

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3Generate Base Predictions

200 rows x 10 columns→Use base models to predict on validation data→200 rows x 3 columns

Predictions from 3 base models for each validation sample

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4Create Meta-Features

200 rows x 3 columns→Use base model predictions as new features→200 rows x 3 columns

Meta-features: pred_tree, pred_logreg, pred_svm

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5Train Meta-Model

200 rows x 3 columns→Train final model on meta-features and true target→Model trained to combine base predictions

Train logistic regression on meta-features

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6Final Prediction

1000 rows x 10 columns→Base models predict, meta-model combines predictions→1000 rows x 1 column

Final predicted probability of buying

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |****
0.3 |****
0.2 |****
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.72	Base models start learning, meta-model not trained yet
2	0.38	0.78	Base models improve, meta-model training begins
3	0.32	0.83	Meta-model learns to combine predictions better
4	0.28	0.86	Loss decreases steadily, accuracy increases
5	0.25	0.88	Training converges with good accuracy

Prediction Trace - 3 Layers

Layer 1: Base Models Predict

Layer 2: Create Meta-Features

Layer 3: Meta-Model Predict

Model Quiz - 3 Questions

What is the main purpose of the meta-model in stacking?

A To combine base model predictions for better accuracy

B To replace base models entirely

C To preprocess the original data

D To generate new raw features

Key Insight

Stacking and blending improve model accuracy by learning how to best combine multiple base models' predictions. Using separate data for meta-model training helps prevent overfitting and leads to more reliable final predictions.