#$1))$ In the code, what does $\text{'}$n$\_$estimators$=100\text{'}$ specify?

Group of answer choices

The number of decision trees in the Random Forest

The maximum depth of the decision trees

The size of the training dataset

The learning rate of the algorithm

#$2))$Which metric is used to assess the model's performance in this code?

Group of answer choices

Accuracy

Root Mean Squared Error $($RMSE$)$

Mean Absolute Error $($MAE$)$

Precision

#$3)))$

How many decision trees are included in the Random Forest model created in this code?

Group of answer choices

$200$

$50$

$100$

$10$

#$4)))$

What is the purpose of the 'random$\_$state' parameter in the Random Forest classifier?

Group of answer choices

It specifies the number of estimators.

It sets the learning rate.

It controls the randomness in the model.

It determines the depth of the decision trees.