📘 What is a Classification Model?

A classification model predicts categories or labels. For example:

• Email → Spam or Not Spam
• Image → Cat or Dog
• Patient → Has disease or Not

📊 Key Metrics for Classification

1. Accuracy – How often is the model correct?
2. Confusion Matrix – Summary of predictions vs actual values.
3. Precision – How many predicted positives are truly positive?
4. Recall (Sensitivity) – How many actual positives were identified correctly?
5. F1 Score – Harmonic mean of precision and recall.
6. ROC Curve and AUC Score – How well the model separates classes.

✅ Example: Evaluating a Classification Model in Python

Let’s use Scikit-learn to demonstrate.

Step 1: Load Sample Dataset

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score

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

# For binary classification, use only two classes
X = X[y != 2]
y = y[y != 2]

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Step 2: Train the Classifier

model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Step 3: Evaluate the Model

1. Accuracy

print("Accuracy:", accuracy_score(y_test, y_pred))

2. Confusion Matrix

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

3. Precision, Recall, F1-Score

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

📌 Output Example Explained

Sample Confusion Matrix (Binary):

[[13 1]
[ 0 16]]

• 13 → True Negative (correctly predicted 0)
• 16 → True Positive (correctly predicted 1)
• 1 → False Positive (predicted 1 but was actually 0)
• 0 → False Negative (predicted 0 but was actually 1)

Sample Metrics:

Accuracy: 0.96
Precision: 0.94
Recall: 1.00
F1-Score: 0.97

📉 ROC Curve and AUC (Optional for binary only)

y_proba = model.predict_proba(X_test)[:, 1]
auc = roc_auc_score(y_test, y_proba)
print("AUC Score:", auc)

🎓 Summary Table (Quick Guide)