Question 5What is the main condition to use stacking as ensemble method?1 pointModels need to be nonparametricModels need to output predicted probabilitiesModels need to be parametricModels need to output residual values for each class

Consider the theoretical setup for a stacking ensemble model designed for a regression task. The first layer of this model includes three different types of regression models: linear regression, ridge regression, and a support vector machine (SVM) with a linear kernel. The second layer, or the final estimator, uses a linear regression model to combine the predictions from the first layer. The goal is to theoretically predict Standard_yield based on features such as Elevation, Slope, Soil_fertility, and Pollution_level, with the intention to evaluate the model's hypothetical performance using the Mean Squared Error (MSE).Given the following theoretical code snippet that outlines this stacking ensemble model's setup, what should replace the _____ in the code to correctly configure the SVM with a linear kernel as part of the base learners in the stacking model?Optionslinearlinsigmoiddegree=1

In general, which of the following is/are the advantage(s) of ensemble techniques?A) Better PredictionB) Lower time of executionC) Simpler than the base modelAnswer choicesSelect only one optionREVISITOnly AB and CA and BOnly C

Which ensemble method involves combining multiple models using weighted voting?Review LaterBaggingBoostingStackingVoting

Which type of Ensemble modeling approach is NOT a special case of model averaging?1 pointBoosting methodsRandom Forest methodsThe Bagging method of Bootstrap aggregationThe Pasting method of Bootstrap aggregation

introduction ensemble methods definition and mechanism principles