Multi-class classification in ML Python - Model Pipeline Trace

Model Pipeline - Multi-class classification

This pipeline shows how a model learns to classify data into more than two categories. It starts with raw data, prepares it, trains a model, and then predicts the correct class for new data.

Data Flow - 6 Stages

1Raw data input

1500 rows x 4 columns→Collect dataset with features and labels for 3 classes→1500 rows x 4 columns

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

↓

2Data preprocessing

1500 rows x 4 columns→Normalize feature values to range 0-1→1500 rows x 4 columns

[0.52, 0.70, 0.14, 0.10, class=0]

↓

3Train/test split

1500 rows x 4 columns→Split data into 1200 training and 300 testing rows→1200 rows x 4 columns (train), 300 rows x 4 columns (test)

[0.52, 0.70, 0.14, 0.10, class=0] (train)

↓

4Feature engineering

1200 rows x 4 columns→No new features added, use all 4 features→1200 rows x 4 columns

[0.52, 0.70, 0.14, 0.10, class=0]

↓

5Model training

1200 rows x 4 columns→Train neural network with softmax output for 3 classes→Model weights updated

Weights adjusted to reduce classification error

↓

6Model evaluation

300 rows x 4 columns→Predict classes and calculate accuracy→Accuracy score (e.g., 0.92)

Predicted classes vs true classes compared

Training Trace - Epoch by Epoch

Loss
1.1 |*       
0.9 | *      
0.7 |  *     
0.5 |   *    
0.3 |    **  
    +--------
     1 5 10 15 20 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.10	0.55	Model starts learning, loss high, accuracy low
5	0.75	0.75	Loss decreases, accuracy improves
10	0.50	0.85	Model converging, better predictions
15	0.35	0.90	Loss low, accuracy high, training stabilizes
20	0.30	0.92	Final epoch, model well trained

Prediction Trace - 4 Layers

Layer 1: Input layer

Layer 2: Hidden layer with ReLU activation

Layer 3: Output layer with softmax

Layer 4: Prediction

Model Quiz - 3 Questions

What does the softmax layer output represent?

A Probabilities for each class that sum to 1

B Raw scores that can be negative

C Binary yes/no decision

D Normalized input features

Key Insight

Multi-class classification uses a model that outputs probabilities for each class. Training reduces error over time, improving accuracy. Softmax ensures outputs are easy to interpret as class probabilities.