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WorksheetsDecision Tree / ensemble methods review
Total questions: 11
Worksheet time: 10mins
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?)
in random forest The number of features that will be randomly selected at each split point must be specified as a parameter to the algorithm. we call it max_features.
For regression problem a good default is:
sqrt(p) : where "p" is the total number of features in the training set (rounded down)
p/3 : where "p" is the total number of features in the training set (rounded down)
