Confusion Matrix and Classification Report

When working with classification models, accuracy alone isn’t always enough to judge performance — especially if your dataset is imbalanced (e.g., predicting rare diseases).
Two useful tools for deeper analysis are:

1. Confusion Matrix – A table showing correct and incorrect predictions for each class.
2. Classification Report – Provides precision, recall, F1-score, and support for each class.

• Confusion Matrix reveals where your model is making mistakes.
• Precision tells you how many predicted positives were correct.
• Recall tells you how many actual positives were correctly identified.
• F1-score balances precision and recall into a single number.

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# Install scikit-learn in Jupyter Lite
import piplite
await piplite.install('scikit-learn')

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix, classification_report

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42, stratify=y
)

# Train a KNN model
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:\n", cm)

# Classification Report
print("\nClassification Report:\n", classification_report(y_test, y_pred))

• Use confusion matrices to visualize misclassifications.
• Precision and recall help understand performance beyond accuracy.
• The F1-score is especially useful for imbalanced datasets.

In the next lesson, we’ll introduce K-Means clustering as our first unsupervised learning algorithm.