You are currently evaluating two classifiers, K-Nearest Neighbours (KNN) and Naive Bayes, for a project that involves classifying texts into different categories based on their content. To finalise your model selection, you decide to visually compare their performance using a bar chart. Below is the setup for calculating the accuracy of both models on your dataset. Complete the code by adding the necessary lines to plot the accuracies in a bar chart:from sklearn.datasets import fetch_20newsgroupsfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.metrics import accuracy_scoreimport matplotlib.pyplot as plt# Load datadata = fetch_20newsgroups(subset='all')X = data.datay = data.target# Create train-test splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Vectorise text datavectorizer = TfidfVectorizer()X_train_tfidf = vectorizer.fit_transform(X_train)X_test_tfidf = vectorizer.transform(X_test)# Initialise classifiersknn = KNeighborsClassifier()nb = MultinomialNB()# Train classifiersknn.fit(X_train_tfidf, y_train)nb.fit(X_train_tfidf, y_train)# Predict and calculate accuracyknn_accuracy = accuracy_score(y_test, knn.predict(X_test_tfidf))nb_accuracy = accuracy_score(y_test, nb.predict(X_test_tfidf))# [Your code here] - Plot the accuracies in a bar chartWhich snippet of code will correctly plot the accuracies of KNN and Naive Bayes classifiers in a bar chart?acc_data = [knn_accuracy, nb_accuracy]labels = ['KNN', 'Naive Bayes']plt.barh(labels, acc_data)plt.xlabel('Accuracy')plt.ylabel('Classifier')plt.title('Accuracy Comparison')plt.show()plt.bar(['KNN', 'Naive Bayes'], [knn_accuracy, nb_accuracy])plt.xlabel('Classifier')plt.ylabel('Accuracy')plt.title('Classifier Accuracies')plt.show()plt.bar(['KNN', 'Naive Bayes'], [knn_accuracy, nb_accuracy])plt.xlabel('Accuracy')plt.ylabel('Classifier')plt.title('Classifier Accuracy Comparison')plt.show()plt.plot(['KNN', 'Naive Bayes'], [knn_accuracy, nb_accuracy])plt.xlabel('Classifier')plt.ylabel('Accuracy')plt.title('Comparison of Classifier Performance')plt.show()

Modify the code below to compute and print the accuracy.from sklearn.datasets import load_breast_cancerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score# Load the datasetdata = load_breast_cancer()X = data.datay = data.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)# Initialise the Logistic Regression modellogreg = LogisticRegression(solver='liblinear')# Train the modellogreg.fit(X_train, y_train)# Predict the test set resultsy_pred = logreg.predict(X_test)# insert code hereWhat is the accuracy of the logistic regression model on the test data?

Given the code below, your task is to select the function from the options provided that correctly completes the task by:i) Creating a function that determines which classifier (KNN or Naive Bayes) has a higher F1 score, or if they have equal scores.ii) Printing the name of the classifier along with its F1 score in the format: 'ClassifierName has the higher F1 score of Score' or 'Both classifiers have the same F1 score of Score'.iii) Executing the function.Select the appropriate code snippet from the options below.from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.naive_bayes import GaussianNBfrom sklearn.metrics import f1_score# Generate a synthetic datasetX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# 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)# Initialise KNN and Naive Bayes classifiersknn = KNeighborsClassifier(n_neighbors=5)nb = GaussianNB()# Train both classifiers on the training dataknn.fit(X_train, y_train)nb.fit(X_train, y_train)# Predict test set results for both classifiersy_pred_knn = knn.predict(X_test)y_pred_nb = nb.predict(X_test)# Calculate F1 scores for both classifiersf1_knn = f1_score(y_test, y_pred_knn)f1_nb = f1_score(y_test, y_pred_nb)# [Your Code Here]def compare_f1_scores(f1_knn, f1_nb):if f1_knn >= f1_nb:print(f"KNN has the higher F1 score of {f1_knn}")else:print(f"Naive Bayes has the higher F1 score of {f1_nb}")compare_f1_scores(f1_knn, f1_nb)def evaluate_classifiers(f1_knn, f1_nb):if f1_knn > f1_nb:print(f"Naive Bayes has the higher F1 score of {f1_nb}")elif f1_nb > f1_knn:print(f"KNN has the higher F1 score of {f1_knn}")else:print(f"Both classifiers have the same F1 score of {f1_nb}")evaluate_classifiers(f1_knn, f1_nb)def print_best_classifier(f1_knn, f1_nb):if f1_knn > f1_nb:print(f"KNN has the higher F1 score of {f1_knn}")elif f1_knn < f1_nb:print(f"Naive Bayes has the higher F1 score of {f1_nb}")else:print(f"Both classifiers have the same F1 score of {f1_knn}")print_best_classifier(f1_knn, f1_nb)def best_f1_score(f1_knn, f1_nb):print(f"KNN: {f1_knn}, Naive Bayes: {f1_nb}")best_f1_score(f1_knn, f1_nb)

Consider the the wine dataset (g = 3, n = 178, p = 13). It is available from the UCI Machine For each value of q for each of the two factor models, list the value of BIC and the MCR (misclassification rate) as compared to the true grouping of the dataset. State and compare the best model for each selection criterion. use R studio

You are tasked with evaluating a simple binary classification model using a confusion matrix. The dataset involves predicting whether a given email is spam or not. To better understand the model's performance, you plan to extract specific metrics from the confusion matrix, specifically True Positives (TP) and False Positives (FP). Below is your initial code setup:from sklearn.metrics import confusion_matrixfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.datasets import make_classification# Generate synthetic binary classification dataX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the dataX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Train a Random Forest classifierclassifier = RandomForestClassifier(random_state=42)classifier.fit(X_train, y_train)# Predict the test set resultsy_pred = classifier.predict(X_test)# Generate the confusion matrixcm = confusion_matrix(y_test, y_pred)# [Your code here] - Extract and print True Positives and False PositivesWhich snippet of code correctly extracts and prints the True Positives (TP) and False Positives (FP) from the confusion matrix?Which snippet of code correctly completes the setup to create a pipeline including PolynomialFeatures and LogisticRegression, fits it on the training data, and makes predictions?print("TP:", cm[2, 2])print("FP:", cm[1, 2])tp = cm[1, 1]fp = cm[0, 1]print("True Positives:", tp)print("False Positives:", fp)print("TP:", cm[1][1])print("FP:", cm[2][1])print("True Positives:", cm[2][2])print("False Positives:", cm[1][2])

For each value of q for each of the two factor models, list the value of BIC and the MCR (misclassification rate) as compared to the true grouping of the dataset. State and compare the best model for each selection criterion. use R studio