✅ Model Evaluation Metrics 📊🤖

👉 After building a Machine Learning model, we must check:
“How good is the model?”

This is done using evaluation metrics.

🔹 1. Why Model Evaluation is Important?
✔ Measures model performance
✔ Detects errors
✔ Helps compare models
✔ Prevents bad predictions

🔥 2. Evaluation Metrics for Regression
Used for predicting numbers

✅ MAE (Mean Absolute Error)
👉 Average absolute error.

MAE = (1/n) Σ |y - ŷ|

✔ Lower MAE = Better model

✅ MSE (Mean Squared Error)
👉 Squares the errors.

MSE = (1/n) Σ (y - ŷ)^2

✔ Punishes large errors more.

✅ RMSE (Root Mean Squared Error)

RMSE = √MSE = √[(1/n) Σ (y - ŷ)^2]

✔ Easy to interpret.

✅ R² Score ⭐
Measures how well model explains data.

R² = 1 - [Σ(y - ŷ)^2 / Σ(y - ȳ)^2]
R² = 1 → Perfect model

✔ Higher R² = Better performance
Where ŷ = predicted value, ȳ = mean of actual values

🔥 3. Evaluation Metrics for Classification
Used for categories

✅ Accuracy

Accuracy = Correct Predictions / Total Predictions

✅ Precision
👉 Out of predicted positives, how many are correct?

Precision = TP / (TP + FP)

✅ Recall
👉 Out of actual positives, how many detected?

Recall = TP / (TP + FN)

✅ F1-Score ⭐
Balance between precision & recall.

F1-Score = 2 (Precision × Recall) / (Precision + Recall)

🔹 4. Confusion Matrix ⭐
A table showing prediction results.

Actual Positive & Predicted Positive = TP (True Positive)
Actual Positive & Predicted Negative = FN (False Negative)
Actual Negative & Predicted Positive = FP (False Positive)
Actual Negative & Predicted Negative = TN (True Negative)

TP = model correctly predicted positive
TN = model correctly predicted negative
FP = model wrongly predicted positive
FN = model wrongly predicted negative

🔹 5. Implementation (Python)

from sklearn.metrics import accuracy_score

y_true = [0, 1, 1, 0]
y_pred = [0, 1, 0, 0]

print(accuracy_score(y_true, y_pred))

🔹 6. Why Metrics Matter?
✔ Helps improve models
✔ Used in interviews
✔ Critical in real-world AI systems

🎯 Today’s Goal
✔ Understand regression metrics
✔ Learn classification metrics
✔ Understand confusion matrix

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