In a regression problem, for a new test data point, the final prediction by a Random Forest is done by taking the _________ Answer choicesSelect only one optionREVISITmode of the individual predictionsminimum of individual predictionsaverage of individual predictionsmedian of individual predictions

Random Forest has _________ as base learning models1 pointmultiple decision treesbaggingentropyNone of these

In regression analysis, the variable that is being predicted is the ___. Group of answer choicesdependent variableindependent variablemoderating variablemediating variable

Decision Trees can be used to to solve ________.Answer choicesSelect only one optionREVISITRegression problemsClassification problemsRegression as well as Classification problemsNeither Regression nor Classification problems

The process of using multiple data samples, generated by random sampling with replacement, from the original dataset to create multiple models and aggregating their predictions is called _______.Answer choicesSelect only one optionREVISITPruningClusteringBaggingOut of bag errors

In this challenge, we want to test how our data fits to a random forest model and other functionalities that come with its such as analysing feature importance.We are required to write a function named train_rf_model that trains and tests a random forest model on a given dataset. Our function should do the following:Take a RandomForestRegressor object (with any desired hyperparameters set) as input,Separate the features X and target y dataframesSplit the data into training and testing sets - use a test size of 20% and a random state of 42 for reproducibilityFit the model to the training dataMake predictions on the testing setReturn the trained model, the R-squared score, and the Mean Squared Error (MSE) of the test set predictions.Question 26Implement the function outlined above.Using the function, train a random forest model on our dataset with random_state set to 42, and max_depth=15, while leaving all other hyperparameters at their defaults. Use all the features available in the encoded dataset for this task. What are the R-squared and MSE scores for the model on the test data?OptionsR2: 0.0059, MSE: 0.6198R2: 0.5555, MSE: 0.2345R2: 0.9586, MSE: 0.0006R2: 0.8196, MSE: 0.0500