What is the main reason that each tree of a random forest only looks at a random subset of the features when building each node?

Practice More Questions From: Module 4 Quiz

Q:

Which of the following is an example of clustering?

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Which of the following are advantages to using decision trees over other models? (Select all that apply)

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What is the main reason that each tree of a random forest only looks at a random subset of the features when building each node?

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Which of the following supervised machine learning methods are greatly affected by feature scaling? (Select all that apply)

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Select which of the following statements are true.

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Match each of the prediction probabilities decision boundaries visualized below with the model that created them.

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A decision tree of depth 2 is visualized below. Using the `value` attribute of each leaf, find the accuracy score for the tree of depth 2 and the accuracy score for a tree of depth 1.

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For the autograded assignment in this module, you will create a classifier to predict whether a given blight ticket will be paid on time (See the module 4 assignment notebook for a more detailed description). Which of the following features should be removed from the training of the model to prevent data leakage? (Select all that apply)

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Which of the following might be good ways to help prevent a data leakage situation?

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Given the neural network below, find the correct outputs for the given values of x1 and x2. The neurons that are shaded have an activation threshold, e.g. the neuron with >1? will be activated and output 1 if the input is greater than 1 and will output 0 otherwise.

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