Write a function that returns the best hyperperameters for a given model (i.e. the GridSearchCV).Function specifications:Should take in an sklearn GridSearchCV object.Should return a dictionary of optimal parameters for the given model.

Before running the final line of the code in the snippet below to fit the grid_search object, you are asked to perform the following tasks directly in the code:1. Modify the param_grid to include a new parameter: 'max_features' with values ranging from 1 to 4.2. Fit the grid_search to the training data.3. After fitting, extract and print the best parameter combination and the corresponding cross-validation score.Which of the following options correctly completes these tasks?from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split, GridSearchCVfrom sklearn.tree import DecisionTreeClassifier# Load the Iris datasetiris = load_iris()X = iris.datay = iris.target# Split the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Setup a basic decision tree classifierdt = DecisionTreeClassifier(random_state=42)# Define a parameter grid over which to optimise the decision treeparam_grid = { 'max_depth': [None, 10, 20, 30], 'min_samples_split': [2, 10, 20]}# Setup the GridSearchCVgrid_search = GridSearchCV(dt, param_grid, cv=5)param_grid.update({'max_features': [1, 2]})grid_search.fit(X_train, y_train)best_params = grid_search.best_params_print(f"Best parameters found: {best_params}, Score: {grid_search.best_score_}")param_grid['max_features'] = range(1, 5)grid_search.fit(X_train, y_train)print(f"Best Params: {grid_search.best_params_}, CV Score: {grid_search.best_score_}")param_grid['max_features'] = [1, 2, 3, 4]grid_search = GridSearchCV(dt, param_grid, cv=5)grid_search.fit(X_train, y_train)print(f"Optimal Parameters: {grid_search.best_params_}, CV Accuracy: {grid_search.best_score_}")param_grid = {'max_features': [1, 2, 3, 4]}grid_search.fit(X_train, y_train)print("Best Parameters:", grid_search.best_params_)print("Best Cross-validation Score:", grid_search.best_score_)

What is the best value of the parameter 'C' for the SVC according to the grid search? Modify the code to print the best parameter.from sklearn.datasets import load_digitsfrom sklearn.model_selection import GridSearchCVfrom sklearn.svm import SVCimport numpy as np# Load a datasetdigits = load_digits()X = digits.datay = digits.target# Initialise an SVC (Support Vector Classifier) with a linear kernelsvm = SVC(kernel='linear')# Define parameter range for C (regularisation parameter)param_grid = {'C': np.logspace(-3, 3, 7)}# Setup the grid search with cross-validationgrid_search = GridSearchCV(svm, param_grid, cv=5, scoring='accuracy')# Fit grid searchgrid_search.fit(X, y)# insert code here

The next step is define a set of SVC hyperparameters to search over. Write a function that searches for optimal parameters using the given dictionary of hyperparameters:C_list = [0.1, 1, 10]{C: 0.1, 1, 10}gamma_list = [0.01, 0.1, 1]{gamma: 0.01, 0.1, 1}D = {'C':[0.1, 1, 10], 'gamma': [0.01, 0.1, 1]}and using custom_scoring_function from Question 3 above as a custom scoring function (Hint: Have a look at at the make_scorer object in sklearn metrics).Function specifications:Should define a parameter grid using the given list of SVC hyperparametersShould return an sklearn GridSearchCV object with a cross validation of 5.Should return a value rounded to 4 decimal places.

Which of the following statements about GridSearchCV is/are True?1 pointGridSearchCV scans over a dictionary of parameters.GridSearchCV finds the hyperparameter set that has the best out-of-sample score.GridSearchCV retrains on all data with the "best" hyper-parameters.All of the above are True.

Question 9Which of the following statements about GridSearchCV is/are True?1 pointGridSearchCV scans over a dictionary of parameters.GridSearchCV finds the hyperparameter set that has the best out-of-sample score.GridSearchCV retrains on all data with the "best" hyper-parameters.All of the above are True.