- Provide the functional form of the predictive model for each algorithm.
- Train each model using different ratios of the trainset and visualize the performance of models using accuracy (y-axis) in terms of different ratios of trainsets (x-axis). Elaborate on the insights
- Apply ensemble methods (bagging, boosting, stacking) on the base models, evaluate the performance of each ensemble technique in 100 Monte Carlo runs, and yjakulig the performance of models using. Boxplot.
- Select the best classifier and elaborate on its advantages and limitations.
Consider a continuous attribute in your dataset as the target variable, perform regression analysis using different ensemble methods, and visualize and interpret the results.