Feature selection methods in ML Python - Model Pipeline Trace

Model Pipeline - Feature selection methods

This pipeline shows how feature selection helps pick the most useful data columns before training a model. It removes less important features to make the model simpler and better.

Data Flow - 5 Stages

1Raw data input

1000 rows x 10 columns→Start with all features collected→1000 rows x 10 columns

Each row has 10 features like age, height, weight, income, etc.

↓

2Feature selection

1000 rows x 10 columns→Select top 5 features based on correlation with target→1000 rows x 5 columns

Kept features: age, income, education, hours worked, credit score

↓

3Train/test split

1000 rows x 5 columns→Split data into 800 training and 200 testing rows→800 rows x 5 columns (train), 200 rows x 5 columns (test)

Training data used to teach model, testing data to check accuracy

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4Model training

800 rows x 5 columns→Train model using selected features→Trained model

Model learns patterns from 5 features to predict target

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5Model evaluation

200 rows x 5 columns→Test model on unseen data→Accuracy and loss metrics

Model predicts target for 200 test rows and calculates accuracy

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
3	0.40	0.80	Model continues to improve with lower loss and higher accuracy
4	0.35	0.83	Training converges with steady improvement
5	0.30	0.86	Final epoch shows best performance with lowest loss

Prediction Trace - 4 Layers

Layer 1: Input features

Layer 2: Model linear layer

Layer 3: Activation (sigmoid)

Layer 4: Threshold decision

Model Quiz - 3 Questions

What is the main purpose of feature selection in this pipeline?

A To increase the number of features for better accuracy

B To keep only the most useful features for training

C To randomly remove features to reduce data size

D To change the target variable

Key Insight

Feature selection helps the model focus on important data, making training faster and predictions more accurate by removing noise from less useful features.