When creating a decision tree, which of the following is a correct way for utilizing the stratified shuffle split. Variables are used as per assignment? A. from sklearn.model_selection import ShuffleSplit ss = ShuffleSplit(n_splits=1, random_state=42) train_idx, test_idx = next(ss.split(data[feature_cols], data[‘color’]))   B. from sklearn.model_selection import StratifiedShuffleSplit sss = StratifiedShuffleSplit(n_splits=1, test_size=1000, random_state=42) train_idx, test_idx = next(sss.split(data[feature_cols], data[‘color’]))   C. from sklearn.model_selection import StratifiedShuffleSplit sss = StratifiedShuffleSplit(test_size=1000, random_state=42) train_idx, test_idx = next(sss.split(data[‘color’]))   D. from sklearn.model_selection import StratifiedShuffleSplit sss = StratifiedShuffleSplit(n_splits=1) train_idx = next(sss.split(data[feature_cols], data[‘color’]))

Which of the following is NOT true about Bagging?  

The main uses for unsupervised learning include:  

How do you calculate the maximum actual depth of a decision tree with object “dt”?

With Bagging, it votes on or averages the result from each tree for each data point and forms a _______________.  

Gradient Boosting is a type of Boosting that works by doing the following:  

With SVMs, you can transform non linear data so it’s linearly separable; this is known as ___________.  

Decision trees are machine learning algorithms that can perform which of the following?

Which of the following are strength(s) of Bagging?  

Stacking (also known as Stacked Generalization) trains the model to: