Lecture Notes on Lift Chart and Classification Metrics

Lift Chart

Lift Chart Explanation
- Used to evaluate the performance of a classification model.
- Blue curve point (10, 5): Selecting 10 observations with the highest probabilities of being class 1, 5 are actual class 1.
- Red curve point (10, 2.2): Randomly selecting 10 observations, 2.2 are expected to be class 1.
Decile-wise Lift Chart
- First decile: 5 observations most likely in class 1.
- Compares actual class 1 to random selection within decile groups.
- First decile lift: 3/1.1, representing the height of the first bar.

Sensitivity/Recall: Ability to predict class 1 correctly, calculated as 1 minus the class 1 error rate.
Specificity: Ability to predict class 0 correctly, calculated as 1 minus the class 0 error rate.
Precision: Proportion of predicted class 1 that are actual class 1.
F1 Score: Combines precision and sensitivity.

Graphical display of classifier's trade-off between sensitivity and specificity.
Area Under the ROC Curve (AUC): Larger AUC indicates better performance.

Average Error: Negative overestimates, positive underestimates an outcome variable.
Root Mean Squared Error (RMSE): Evaluates model performance.

Purpose: Classify binary categorical outcomes (0 or 1).
Odds and Logit Function: Odds defined as P/(1-P), logistic regression uses logit function to model probabilities.
Logistic Function: S-shaped curve fitting probabilities between 0 and 1.
Model Implementation: Uses explanatory variables to determine best estimates for coefficients.

Purpose: Classify categorical outcomes or estimate continuous outcomes.
Euclidean Distance: Measures similarity.
K-Value: Defines the number of nearest neighbors considered.
Classification: New observation classifies based on majority class of nearest neighbors.