Confusion Matrix

Introduction

A confusion matrix counts prediction outcomes in four buckets: true positive, false positive, true negative, and false negative. It is the foundation for precision, recall, and many other classification metrics.

Understanding the topic

Read the grid Rows are actual classes; columns are predicted classes (sklearn default).

Imbalanced data Accuracy alone can hide poor minority-class performance — always inspect the matrix.

• Read the grid — Rows are actual classes; columns are predicted classes (sklearn default).
• Imbalanced data — Accuracy alone can hide poor minority-class performance — always inspect the matrix.

Step-by-step explanation

1. Read the grid — Rows are actual classes; columns are predicted classes (sklearn default).
2. Imbalanced data — Accuracy alone can hide poor minority-class performance — always inspect the matrix.

Informative example

Python starter:

from sklearn.metrics import confusion_matrix
import numpy as np

y_true = np.array([1, 0, 1, 1, 0, 0])
y_pred = np.array([1, 0, 0, 1, 0, 1])
print(confusion_matrix(y_true, y_pred).tolist())

[[2, 1], [1, 2]]

Execution workflow

Best practices

• Hold out a test set before hyperparameter tuning.
• Scale numeric columns for distance-based models.
• Track multiple metrics — not accuracy alone on skewed labels.

Common mistakes

• Leaking test statistics into preprocessing fit on full data.
• Training on the same rows you report as test performance.
• Chasing complex models before a simple baseline.

Hands-on exercise

Practice:

• Compute TN/FP/FN/TP manually for a tiny example
• Plot with seaborn.heatmap

Summary

Confusion Matrix — TP/FP/TN/FN grid for classifiers.