Decision Tree / ensemble methods review

What is a Decision Tree?

• Flow-Chart

• Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label

• Flow-Chart & Structure in which internal node represents test on a feature, each branch represents outcome of test and each leaf node represents class label

• None of the mentioned

Decision Trees can be used for regression Tasks

• True

• False

Decision tree usually underfit the data

• True

• False

Decision trees are example of model with high variance.

• True

• False

in a decision tree, If the number of levels is too low i.e. the depth of the tree is too low, the model tends to overfit.

• True

• False

decision tree is computationally cheap for predictions but training the tree can be computationally expensive

• True

• False

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model

• True

• False

"bagging" ensemble method focuses on decreasing the bais

• True

• False

Ensemble methods can be divided into two groups, sequential ensemble methods & parallel ensemble methods.

for the sequential ensemble methods, the base learners (estimators) are generated sequentially (e.g. AdaBoost).

• True

• False

In random forests, each tree in the ensemble is built from a different sample drawn with replacement (i.e. a bootstrap sample) from the training set

• True

• False

• Don't know the answer but i like the meme

• Don't know the answer & i'm not a meme person (really?)