Precision-recall curves in TensorFlow - Model Pipeline Trace

Model Pipeline - Precision-recall curves

This pipeline trains a binary classifier and evaluates it using precision-recall curves. Precision-recall curves help us understand the trade-off between correctly identifying positive cases and avoiding false alarms.

Data Flow - 6 Stages

1Data Loading

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

Feature row: [0.5, 1.2, ..., 0.3], Label: 1

↓

2Train/Test Split

1000 rows x 10 columns→Split data into 800 training and 200 testing rows→Train: 800 rows x 10 columns, Test: 200 rows x 10 columns

Train feature row: [0.7, 0.1, ..., 0.4], Train label: 0

↓

3Feature Normalization

800 rows x 10 columns→Scale features to zero mean and unit variance→800 rows x 10 columns

Normalized feature row: [0.1, -0.3, ..., 0.0]

↓

4Model Training

800 rows x 10 columns→Train binary classifier with sigmoid output→Trained model

Model weights updated after each batch

↓

5Prediction on Test Set

200 rows x 10 columns (normalized)→Apply feature normalization to test set using training statistics, then generate predicted probabilities for positive class→200 rows x 1 column

Predicted probability: 0.85

↓

6Precision-Recall Curve Computation

200 rows x 1 column (probabilities) + 200 labels→Calculate precision and recall at different thresholds→Array of precision and recall pairs

Threshold 0.5: Precision=0.8, Recall=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 moderate loss and accuracy
2	0.50	0.72	Loss decreased, accuracy improved
3	0.40	0.80	Model learning well, loss dropping
4	0.35	0.83	Continued improvement in metrics
5	0.30	0.86	Training converging with good accuracy

Prediction Trace - 4 Layers

Layer 1: Input Features

Layer 2: Feature Normalization

Layer 3: Model Prediction (Sigmoid)

Layer 4: Thresholding for Classification

Model Quiz - 3 Questions

What does a point on the precision-recall curve represent?

A Loss and accuracy at a training epoch

B Precision and recall values at a specific threshold

C Input features after normalization

D Model weights after training

Key Insight

Precision-recall curves are useful to evaluate models especially when classes are imbalanced. They show how precision and recall change as the decision threshold varies, helping to choose the best threshold for the task.