Can we pass data with null values to machine learning model building?

Decision Trees are often prone to high variance, and the model is not likely to generalize well because they tend to:

Which of the following clustering methods is most ideal for finding core samples of high density and expanding clusters from them?  

Which of the following is true about Multi Dimensional Scaling?

What is/are the main approach(es) for dimensionality reduction:  

Clustering is a process that:  

A popular dimensionality reduction algorithm that identifies the hyperplane that lies closest to the data and then projects the data onto it is known as __________________.  

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: