We want to examine how our data will fit to a random forest model when we tune the number of trees. We want to train and compare two random forest models with the same dataset as in the previous exercise. The first model should be trained with 150 trees, and the second model with 200 trees. Both models should use the default hyperparameters for all other settings, apart from a random_state of 42 to ensure reproducibility. After evaluating both models on the test set, how does the error differ between the two models?The model with 200 trees showed a significant decrease in error compared to the model with 150 trees.The model with 200 trees showed a very slight decrease in error compared to the model with 150 trees.There was no change in the error.The error increased when the number of trees was increased from 150 to 200.

Do the trees from the two models differ

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

14. A Junior analyst trained an Sklearn random forest classifier to predict the winner of football matches. The model is performing well on the train data set. When the model was evaluated on the test dataset it performed poorly. Which of the following will be useful in solving this problem?. Select all correct.increasing the min samples leaf in each tree of the forestreducing the number of trees in the forestdecreasing the max depth of each tree in the forestreducing the min samples split in each tree of the forest

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

What is a characteristic of Random Forests?Each tree is trained on a random subset of the featuresAll trees are identicalThey are sensitive to feature scalingThey are prone to overfitting