Load the California housing dataset provided in sklearn.datasets, and construct a random 70/30 train-test split. Set...

 

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Load the California housing dataset provided in sklearn.datasets, and construct a random 70/30 train-test split. Set the random seed to a number of choice to make the split reproducible. What is the value of d here? your Train a random forest of 100 decision trees using default hyperparameters. Report the training and test MSEs. What is the value of m used? Write code to compute the pairwise (Pearson) correlations between the test set predictions of all pairs of distinct trees. Report the average of all these pairwise correlations. You can retrieve all the trees in a RandomForest Classifier object using the estimators\_ attribute. Load the California housing dataset provided in sklearn.datasets, and construct a random 70/30 train-test split. Set the random seed to a number of choice to make the split reproducible. What is the value of d here? your Train a random forest of 100 decision trees using default hyperparameters. Report the training and test MSEs. What is the value of m used? Write code to compute the pairwise (Pearson) correlations between the test set predictions of all pairs of distinct trees. Report the average of all these pairwise correlations. You can retrieve all the trees in a RandomForest Classifier object using the estimators\_ attribute.

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Answer Expert Answer General Guidance The answer provided below has been developed in a clear step by step manner Step 1 The program loads the Califor... View the full answer