A/B testing models in ML Python - Model Pipeline Trace

Model Pipeline - A/B testing models

A/B testing models means comparing two different machine learning models to see which one works better. We split data into two groups, train each model separately, then check which model gives better results.

Data Flow - 5 Stages

1Data Collection

1000 rows x 10 columns→Collect raw data with features and labels→1000 rows x 10 columns

Each row has 10 features like age, income, clicks, and a label like purchase (0 or 1)

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2Train/Test Split

1000 rows x 10 columns→Split data into training (80%) and testing (20%) sets→Training: 800 rows x 10 columns, Testing: 200 rows x 10 columns

800 rows used to train models, 200 rows saved for final evaluation

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3Data Split for A/B

Training: 800 rows x 10 columns→Split training data into two groups for Model A and Model B→Model A: 400 rows x 10 columns, Model B: 400 rows x 10 columns

Randomly split the 800 training rows into two sets of 400 rows each: one for Model A and one for Model B

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4Model Training

Model A: 400 rows x 10 columns, Model B: 400 rows x 10 columns→Train two different models on their respective data→Two trained models

Model A is a decision tree, Model B is a logistic regression

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5Model Evaluation

Testing: 200 rows x 10 columns→Evaluate both models on the same test data→Performance metrics for Model A and Model B

Model A accuracy: 0.85, Model B accuracy: 0.80

Training Trace - Epoch by Epoch

Loss
0.7 | *       
0.6 | **      
0.5 | ***     
0.4 | ****    
0.3 | *****   
    +---------
     1 2 3 4 5
     Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Both models start with moderate loss and accuracy
2	0.50	0.70	Loss decreases and accuracy improves for both models
3	0.40	0.78	Model A shows slightly better improvement than Model B
4	0.35	0.82	Model A continues to improve; Model B improves slower
5	0.30	0.85	Model A converges with better accuracy; Model B lags behind

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Model A Prediction

Layer 3: Model B Prediction

Layer 4: Decision

Model Quiz - 3 Questions

What is the main purpose of splitting training data into two groups in A/B testing?

A To train two different models separately

B To increase the size of the training data

C To test the models on unseen data

D To reduce the number of features

Key Insight

A/B testing models helps us compare two different machine learning models fairly by training them on separate data splits and evaluating them on the same test data. This way, we can pick the model that performs better in real situations.