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

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

After using EMMIXmfa of R studio to make mfa and mcfa models, and choose q from 1 to 6, then next question is 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. how to solve

For the MCFA model fited with q = 2 factors, plot the three clusters in the factor space with the clusters distinguished by different symbols or colours. Repeat the plot but this time with the true labels shown instead of the cluster labels. use EMMIXmfa of R studio

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()

For the MCFA model fited with q = 2 factors, plot the three clusters in the factor space with the clusters distinguished by different symbols or colours. Repeat the plot but this time with the true labels shown instead of the cluster labels use EMMIXmfa of R studio