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

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

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

What is an ensemble model that needs you to look at out of bag error?1 pointStackingLogistic Regression.Out of Bag RegressionRandom Forest

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

Consider the following Python code snippet that aims to implement a 5-fold cross-validation scheme to estimate the accuracy of a ridge regression model. This model uses bootstrapped samples within each fold to predict Standard_yield and calculates the average Mean Squared Error (MSE) across all folds.Which of the following options correctly fill in the blanks to ensure the code correctly implements the described functionality?Optionsscoring='r2', cv=10scoring='mean_absolute_error', cv=5scoring='neg_mean_squared_error', cv=5scoring='neg_mean_squared_error', cv='Bootstrap'