Machine Learning · Chapter 25 of 40
Confusion Matrix
A CONFUSION MATRIX shows TRUE vs PREDICTED labels for each class. Four cells for binary classification: TP, FP, TN, FN.
From it you can compute accuracy, precision, recall and F1.
Example 1 (python)
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred))Output
[[85 5]
[ 8 42]]Rows: true. Columns: predicted.
Example 2 (python)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))Output
precision recall f1-score support...Full metrics per class.
Key points
- Shows true vs predicted labels.
- TP/FP/TN/FN.
- Foundation for precision, recall, F1.
- Read rows as truth, columns as prediction.
💡 Note: In imbalanced problems, accuracy alone lies. Always inspect the confusion matrix and per-class metrics.