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Supervised Learning – Regression (Marketing Analysis Sales Prediction & Automobiles Price Prediction)
Model Evaluation Metrics (R²-score, MSE, MAE, RMSE)
Learning Outcome
5
Evaluate model performance using the R² score.
4
Convert and interpret MSE using Root Mean Squared Error (RMSE).
3
Explain why Mean Squared Error (MSE) penalizes large outliers.
2
Calculate and interpret Mean Absolute Error (MAE).
1
Define a Residual (error) in predictive modeling.
Lets Recall....
The Reality Check
The .predict() function always outputs a number, even if it's a terrible guess.
The Story So Far
We've built Simple, Multiple, Polynomial, and Regularized regression models to predict car prices.
Hook/Story/Analogy(Slide 4)
Transition from Analogy to Technical Concept(Slide 5)
The Concept
Average distance between predictions and actual values
The Automobile Interpretation
MAE = 1,200
"On average, our model's price prediction is off by $1,200"
The "Everyday" Metric
Most intuitive error measurement
The Formula
MAE = 1/n Σ |Actual - Predicted|
Metric 1: MAE (Mean Absolute Error)
Core Concepts (.....Slide N-3)
Summary
5
Use R² to get a universal "percentage" score of your model's quality.
4
Use RMSE if you want to aggressively penalize massive outliers.
3
Use MAE for a simple average error.
2
Residuals are the gap between reality and our AI's prediction.
1
You cannot improve what you cannot measure.
Quiz
If your manager asks, "What percentage of the variation in car prices is our algorithm actually able to explain?", which metric should you give them?
A. MSE
B. R²-score
C. Adjusted Residuals
D. RMSE
Quiz-Answer
If your manager asks, "What percentage of the variation in car prices is our algorithm actually able to explain?", which metric should you give them?
A. MSE
B. R²-score
C. Adjusted Residuals
D. RMSE
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