Threshold tuning in ML Python - Model Pipeline Trace

Model Pipeline - Threshold tuning

This pipeline shows how adjusting the decision threshold of a classification model affects its predictions and performance metrics. Instead of using the default 0.5 cutoff, we tune the threshold to balance precision and recall better.

Data Flow - 6 Stages

1Raw data input

1000 rows x 10 columns→Load dataset with features and binary labels→1000 rows x 10 columns

Feature1=0.5, Feature2=1.2, ..., Label=1

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2Train/test split

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

Train sample: Feature1=0.3, Label=0; Test sample: Feature1=0.7, Label=1

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3Model training

Train: 800 rows x 10 columns→Train logistic regression model on training data→Trained model

Model learns weights for each feature

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4Prediction probabilities

Test: 200 rows x 10 columns→Model outputs probability scores for positive class→200 rows x 1 column (probabilities)

Sample probability: 0.72

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5Threshold tuning

200 rows x 1 column (probabilities)→Apply different thresholds to convert probabilities to class labels→200 rows x 1 column (predicted labels)

Threshold=0.3: predicted label=1; Threshold=0.7: predicted label=0

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6Metric calculation

200 rows x 1 column (predicted labels), 200 rows x 1 column (true labels)→Calculate precision, recall, and accuracy for each threshold→Metrics summary per threshold

Threshold=0.5: Precision=0.8, Recall=0.7, Accuracy=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	Initial training with random weights
2	0.50	0.72	Model starts learning useful patterns
3	0.40	0.80	Loss decreases and accuracy improves
4	0.35	0.83	Model converging well
5	0.33	0.85	Training stabilizes with good accuracy

Prediction Trace - 3 Layers

Layer 1: Model prediction

Layer 2: Apply threshold 0.5

Layer 3: Apply threshold 0.7

Model Quiz - 3 Questions

What happens to the number of positive predictions if we lower the threshold from 0.7 to 0.3?

A More samples are predicted positive

B Fewer samples are predicted positive

C Number of positive predictions stays the same

D Model accuracy decreases automatically

Key Insight

Threshold tuning helps customize model predictions to fit specific needs by adjusting the cutoff point for classifying positive cases. This can improve important metrics like precision or recall depending on the problem.