1. Provide the functional form of the predictive model for each algorithm.
2. 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
3. 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.
4. 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.