Label encoding in ML Python - Model Pipeline Trace

Model Pipeline - Label encoding

Label encoding changes words or categories into numbers so a computer can understand them. It replaces each unique category with a unique number.

Data Flow - 2 Stages

1Raw Data

5 rows x 1 column→Original categorical data with animal names→5 rows x 1 column

["cat", "dog", "bird", "cat", "dog"]

↓

2Label Encoding

5 rows x 1 column→Replace each unique category with a unique integer→5 rows x 1 column

[0, 1, 2, 0, 1]

Training Trace - Epoch by Epoch

Loss
0.8 |*****
0.6 |****
0.4 |***
0.3 |**
0.25|*
     +---------
     Epochs 1-5

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.8	0.40	Model starts with low accuracy using encoded labels
2	0.6	0.55	Accuracy improves as model learns from encoded labels
3	0.4	0.75	Loss decreases and accuracy increases steadily
4	0.3	0.85	Model converges well with label encoded data
5	0.25	0.90	Final epoch shows good performance using label encoding

Prediction Trace - 3 Layers

Layer 1: Input sample

Layer 2: Label Encoding

Layer 3: Model prediction

Model Quiz - 3 Questions

What does label encoding do to categorical data?

A Converts categories into unique numbers

B Removes categories from data

C Changes numbers into categories

D Splits data into training and test sets

Key Insight

Label encoding helps convert words into numbers so models can understand and learn from categorical data effectively.