Random forest in depth in ML Python - Model Pipeline Trace

Model Pipeline - Random forest in depth

A random forest is a group of decision trees working together to make better predictions. It uses many trees to reduce mistakes and improve accuracy by averaging their results.

Data Flow - 6 Stages

1Data input

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

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

↓

2Train/test split

1000 rows x 10 columns→Split data into training and testing sets→800 rows x 10 columns (train), 200 rows x 10 columns (test)

Train: 800 samples, Test: 200 samples

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3Bootstrap sampling

800 rows x 10 columns→Randomly sample with replacement to create subsets for each tree→800 rows x 10 columns per tree subset

Subset for tree 1: 800 samples with repeats

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4Feature selection per split

800 rows x 10 columns→Randomly select subset of features for each tree split→800 rows x 3 columns (random features per split)

At split: features age, score, income chosen

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5Decision tree training

800 rows x 3 columns→Train each tree on its bootstrap sample and selected features→Trained decision tree model

Tree 1 trained with subset and features

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6Forest aggregation

Multiple trained trees→Combine predictions from all trees by majority vote or averaging→Final prediction for each sample

Prediction: 7 out of 10 trees say 'buy'

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	0.75	Initial trees start to learn patterns
2	0.38	0.80	More trees reduce error and improve accuracy
3	0.33	0.83	Model converges with stable improvements
4	0.30	0.85	Small gains as trees refine decisions
5	0.28	0.86	Training stabilizes with low loss and high accuracy

Prediction Trace - 3 Layers

Layer 1: Input sample

Layer 2: Each tree prediction

Layer 3: Aggregation

Model Quiz - 3 Questions

Why does a random forest use many trees instead of one?

A To reduce mistakes by averaging many decisions

B To make the model slower

C To use all features at once

D To avoid splitting data

Key Insight

Random forests improve prediction by combining many decision trees trained on random data and features. This reduces errors and makes the model more reliable than a single tree.