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

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_)

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.

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.

What is the primary purpose of the GridSearchCV() method?1 pointTo split your data into folds, then iterate over the folds to train and test your model.To determine the appropriate order of a model.To help select appropriate hyperparameter values.To determine if you’ve underfit or overfit your data.

How to import GridSearchCV?Review Laterfrom sklearn.model_selection import GridSearchCVfrom sklearn import GridSearchCVfrom sklearn.model import GridSearchCVfrom sklearn.linear_model import GridSearchCV