Bagging concept in ML Python - Model Pipeline Trace

Model Pipeline - Bagging concept

Bagging is a way to make a machine learning model stronger by training many models on different random parts of the data and then combining their answers.

Data Flow - 5 Stages

1Original Dataset

1000 rows x 10 columns→Start with full dataset→1000 rows x 10 columns

Each row is a house with 10 features like size, rooms, age, etc.

↓

2Bootstrap Sampling

1000 rows x 10 columns→Randomly pick 1000 rows with replacement to create a new dataset→1000 rows x 10 columns

Some houses appear multiple times, some not at all

↓

3Train Base Model

1000 rows x 10 columns→Train a model on the bootstrap sample→Trained model

A decision tree learns patterns from the sampled houses

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4Repeat Sampling and Training

1000 rows x 10 columns→Repeat bootstrap sampling and training multiple times (e.g., 10 models)→10 trained models

Each model sees a slightly different dataset

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5Combine Predictions

New data sample→Each model predicts, then combine predictions by majority vote or averaging→Final prediction

For house price, average predictions from all models

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.45|****
0.4 |***
0.35|**
0.3 |*
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.70	First model trained on first bootstrap sample
2	0.43	0.72	Second model trained on different bootstrap sample
3	0.40	0.74	Third model trained, overall ensemble accuracy improves
4	0.38	0.76	More models added, ensemble becomes stronger
5	0.36	0.78	Loss decreases steadily, accuracy increases

Prediction Trace - 3 Layers

Layer 1: Input new data sample

Layer 2: Each model predicts

Layer 3: Combine predictions

Model Quiz - 3 Questions

What is the main purpose of bootstrap sampling in bagging?

A To reduce the number of features used in training

B To create different training sets by random sampling with replacement

C To speed up training by using fewer data points

D To test the model on unseen data

Key Insight

Bagging reduces errors by averaging many models trained on different random samples, which lowers variance and improves prediction stability.