Part $1$: Decision Trees $($DT$)-4$pts$.$ each task

To complete the following tasks on DTs$,$ you can use Python and libraries such as scikit$-$learn to implement your code.

Task $1$: Hyperparameter Tuning $($Random Search$)$

$1.$Load the Iris dataset.

$2.$Split the data into training and testing sets.

$3.$Implement a DT classifier.

$4.$Perform a Random Search to find the best hyperparameters for the DT classifier. Search for hyperparameters like max depth, min samples split, min samples leaf, and criterion. Hint: Use the RandomizedSearchCV function from scikit$-$learn.

$5.$Print the best hyperparameters and the model's accuracy with these hyperparameters.

Task $2$: Error Analysis

$1.$After training the DT model with the best hyperparameters from Task $1,$ use this model to make predictions on the test data.

$2.$Identify and print the indices of misclassified instances $($where the true class is not equal to the predicted class$).$

Task $3$: Confusion Matrix

$1.$ Calculate the confusion matrix for the model's predictions on the test data.

$2.$Print the confusion matrix values $($True Positives, True Negatives, False Positives, False Negatives$).$

Note: The following Tasks $4$ and $5$ were not taught extensively in class for DTs$.$ However, the concepts were covered in liner regression, so I$\text{'}$d like you to give these a try w$.$r$.$ DTs$.$

Task $4$: Regression with DTs

$1.$Load a dataset suitable for regression $($e$.$g$,$ the Boston housing dataset from scikit$-$learn$).$

$2.$Split the dataset into training and testing sets.

$3.$Implement a DT regression model.

$4.$Train the model on the training data.

$5.$Calculate and print the mean squared error $($MSE$)$ on the testing data to assess the model's performance.

Task $5$: Metrics Comparison

$1.$Compare the performance of the DT classifier from Task $1$ and the DT regression model from Task $4.$

$2.$Calculate and print relevant evaluation metrics for the classifier $($e$.$g$.,$ accuracy, precision, recall, F$1-$score$)$ and the regression model $($e$.$g$.$ MSE$).$

$3.$Discuss the results, including which model performed better and why.