Creating interaction features in ML Python - Model Pipeline Walkthrough

Model Pipeline - Creating interaction features

This pipeline shows how we create new features by combining existing ones to help the model learn better. Interaction features capture relationships between original features, improving prediction power.

Data Flow - 4 Stages

1Raw data input

1000 rows x 3 columns→Initial dataset with three features: Age, Income, and Education Level→1000 rows x 3 columns

Age=25, Income=50000, Education=3

↓

2Feature scaling

1000 rows x 3 columns→Normalize Age and Income to range 0-1→1000 rows x 3 columns

Age=0.25, Income=0.5, Education=3

↓

3Create interaction features

1000 rows x 3 columns→Multiply Age and Income, Age and Education to create new features→1000 rows x 5 columns

Age=0.25, Income=0.5, Education=3, Age*Income=0.125, Age*Education=0.75

↓

4Train/test split

1000 rows x 5 columns→Split data into 800 training rows and 200 testing rows→Train: 800 rows x 5 columns, Test: 200 rows x 5 columns

Train row example: Age=0.25, Income=0.5, Education=3, Age*Income=0.125, Age*Education=0.75

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	Model starts learning with moderate loss and accuracy
2	0.50	0.72	Loss decreases and accuracy improves as model learns interaction features
3	0.40	0.80	Model continues to improve, showing better fit
4	0.35	0.85	Loss decreases steadily, accuracy rises
5	0.30	0.88	Training converges with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input features

Layer 2: Create interaction features

Layer 3: Model input vector

Layer 4: Model prediction

Model Quiz - 3 Questions

What is the main purpose of creating interaction features?

A To normalize the data

B To reduce the number of features

C To capture relationships between original features

D To split data into train and test sets

Key Insight

Creating interaction features helps the model learn complex relationships between variables, improving accuracy and reducing loss during training.