What the confusion matrix shows

A confusion matrix compares:

• actual labels
• predicted labels

For binary classification:

• TP: true positives
• TN: true negatives
• FP: false positives
• FN: false negatives

  flowchart TD
  A[Actual Positive] -->|Pred Positive| TP[TP]
  A -->|Pred Negative| FN[FN]
  B[Actual Negative] -->|Pred Negative| TN[TN]
  B -->|Pred Positive| FP[FP]

Why it matters

All important classification metrics come from these four numbers.

Scikit-learn example

from sklearn.metrics import confusion_matrix
 
cm = confusion_matrix(y_true, y_pred)
print(cm)

from sklearn.metrics import confusion_matrix
 
cm = confusion_matrix(y_true, y_pred)
print(cm)

Mini-checkpoint

If FN is very high, what does that mean?

• You’re missing many real positives.

🧪 Try It Yourself

Exercise 1 – Train-Test Split

Exercise 2 – Fit a Linear Model

Exercise 3 – Evaluate with MSE