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Random Forests

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Welcome to your Random Forests Part I

What is 'random' about a random forest? (select all that apply)

the data used to train the model

the target variable

the number of trees created

the maximum leaf depth

the features considered when splitting a node

Bagging is an abbreviation of

Backpropagation gradient

Bayesian aggregation

Bootstrapped aggregation

Boosted aggregation

Bag of Words Grouping

Does a random forest use boosting or bagging?

boosting

bagging

How is out-of-bag error determined?

Scoring the trees on observations that were not used in training those trees

Scoring the random forest model on the observations used to train the model

Scoring the trees on observations that were used in training those trees

Scoring the random forest model on the observations that were not used to train the model

What is a suggested starting point for the number of variables used per step?

one tenth the number of observations in the dataset

the square of the number of variables

the square root of the number of variables

one half the number of variables

the square root of the number of observations in the dataset

Which of the following are hyperparameters for a random forest? (select all that apply)

max leaf depth

learning_rate

the number of trees in the forrest

the type of weak classifier

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This entry was posted on November 12, 2020 by lukas.halim@gmail.com.

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Drill to increase your skill! Wikipedia explains, “The testing effect is the finding that long-term memory is often increased when some of the learning period is devoted to retrieving the to-be-remembered information.” In other words, you remember what you learn better when you answer questions than when you spend the same time watching videos or reading blog posts. Answering open-ended questions (explain a random forest) is generally more effective than answering multiple-choice questions.

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