One-vs-rest and one-vs-one strategies in ML Python - Model Pipeline Trace

Model Pipeline - One-vs-rest and one-vs-one strategies

This pipeline shows how a multi-class classification problem is solved using two popular strategies: one-vs-rest and one-vs-one. Both break the problem into simpler binary tasks, train models, and combine their results to predict the class.

Data Flow - 5 Stages

1Input Data

150 rows x 4 columns→Original dataset with 3 classes and 4 features→150 rows x 4 columns

[5.1, 3.5, 1.4, 0.2, class=0]

↓

2One-vs-Rest Label Transformation

150 rows x 4 columns→Create 3 binary label sets, each for one class vs rest→3 sets of 150 rows x 4 columns + 1 binary label column

For class 0: labels are 1 if class=0 else 0

↓

3One-vs-One Label Transformation

150 rows x 4 columns→Create 3 binary datasets for each pair of classes→3 sets of ~100 rows x 4 columns + 1 binary label column

For class 0 vs 1: only samples from classes 0 and 1 with labels 0 or 1

↓

4Model Training

Binary datasets from previous steps→Train one binary classifier per dataset→3 binary classifiers for OvR, 3 binary classifiers for OvO

Each classifier learns to separate its two classes

↓

5Prediction Aggregation

New sample with 4 features→Combine binary classifier outputs to predict final class→Single predicted class label (0, 1, or 2)

OvR: class with highest confidence; OvO: class with most votes

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |**  
0.3 |*   
0.2 |*   
0.1 |    
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Initial training with random weights, moderate accuracy
2	0.45	0.75	Loss decreased, accuracy improved as model learns
3	0.30	0.85	Model converging, better separation of classes
4	0.20	0.90	High accuracy, low loss, training stabilizing
5	0.15	0.92	Final epoch, model well trained

Prediction Trace - 5 Layers

Layer 1: Input Sample

Layer 2: One-vs-Rest Classifiers

Layer 3: OvR Final Prediction

Layer 4: One-vs-One Classifiers

Layer 5: OvO Final Prediction

Model Quiz - 3 Questions

In one-vs-rest strategy, how many binary classifiers are trained for 4 classes?

A 2

B 6

C 4

D 1

Key Insight

One-vs-rest and one-vs-one strategies simplify multi-class problems into binary tasks. OvR trains one classifier per class against all others, while OvO trains classifiers for every class pair. OvR is simpler but can be less precise; OvO is more detailed but requires more models. Both improve multi-class classification by leveraging binary classifiers.