The hyperparameters of the Random Forest algorithm include:

1. Number of estimators: This refers to the number of trees in the forest. It is usually set to a high value to improve performance, but at the cost of computational power.

2. Maximum depth: This refers to the maximum number of levels in each decision tree. You can set it to a specific number or leave it undefined, in which case the nodes will expand until all leaves are pure or until all leaves contain less than a certain number of samples.

3. Subsample size: This is not a direct hyperparameter of the Random Forest algorithm, but it is a parameter of the bootstrap sampling method used in Random Forest. It refers to the number of samples to draw from the total dataset to train each tree.

The learning rate is not a hyperparameter of the Random Forest algorithm. It is typically associated with algorithms that use gradient descent for optimization, such as neural networks or gradient boosting.

Question

The hyperparameters of the Random Forest algorithm include:

1. Number of estimators: This refers to the number of trees in the forest. It is usually set to a high value to improve performance, but at the cost of computational power.

2. Maximum depth: This refers to the maximum number of levels in each decision tree. You can set it to a specific number or leave it undefined, in which case the nodes will expand until all leaves are pure or until all leaves contain less than a certain number of samples.

3. Subsample size: This is not a direct hyperparameter of the Random Forest algorithm, but it is a parameter of the bootstrap sampling method used in Random Forest. It refers to the number of samples to draw from the total dataset to train each tree.

The learning rate is not a hyperparameter of the Random Forest algorithm. It is typically associated with algorithms that use gradient descent for optimization, such as neural networks or gradient boosting.

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The hyperparameters of the Random Forest algorithm include:

1. Number of estimators: This refers to the number of trees in the forest. It is usually set to a high value to improve performance, but at the cost of computational power.

2. Maximum depth: This refers to the maximum number of levels in each decision tree. You can set it to a specific number or leave it undefined, in which case the nodes will expand until all leaves are pure or until all leaves contain less than a certain number of samples.

3. Subsample size: This is not a direct hyperparameter of the Random Forest algorithm, but it is a parameter of the bootstrap sampling method used in Random Forest. It refers to the number of samples to draw from the total dataset to train each tree.

The learning rate is not a hyperparameter of the Random Forest algorithm. It is typically associated with algorithms that use gradient descent for optimization, such as neural networks or gradient boosting.

Which of the following is a hyperparameter of the Random Forest algorithm?Review LaterLearning rateNumber of estimatorsMaximum depthSubsample size

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Which of the following is a hyperparameter of the Random Forest algorithm?

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