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.

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.

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

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_)

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)