Regression
Regression Algorithms
Regression Trees
k-NN: K-Nearest Neighbors
Performance Evaluation
ME: Mean Error
MAE: Mean Absolute Error
MAPE: Mean Absolute Percentage Error
Total SSE: Total Sum of Squared Error
RMSE: Root Mean Squared Error
Regression (Numeric Prediction):
Predicts continuous/numeric values of the dependent variable
Ex: How much will the customer buy? (Quantity or $)
- Model Construction (Training step)
- Model Testing (Testing step)
the outcomes in the leaf nodes are determined as the average of the outcome values of the data points in that node
splits are made using the Sum of squared deviations (SSD) from the average outcome value at that node
- Pick distance metric and choose k2. Normalize data if needed 3. for every new observation, identify k nearest existing observation 4. predict outcome by taking average of nearest neighbor outcome values
