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?

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()

This question refers to the following code snippet, which assumes that all required libraries have been imported.Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,test_size = 0.3)yhat = GaussianNB().fit(Xtrain,ytrain).predict(Xtest)acc = accuracy_score(ytest, yhat)This code uses with of available data used for training. It outputs the based on . Every time we run this code, we will get .

Which of the following options will complete the missing code lines to:i) train the MLPClassifier,ii) predict the test set labels,iii) count the number of misclassified samples,iv) call the function to print the results.from sklearn.datasets import make_moonsfrom sklearn.model_selection import train_test_splitfrom sklearn.neural_network import MLPClassifierfrom sklearn.preprocessing import StandardScalerimport numpy as np# Generate a two-moon datasetX, y = make_moons(n_samples=1000, noise=0.2, random_state=42)# Split the dataset into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)# Scale the featuresscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)# Initialise the MLPClassifier with one hidden layer with 10 neuronsmlp = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)# [Your Code Here] - Train the MLPClassifier on the scaled training data# [Your Code Here] - Predict the labels for the scaled test data# [Your Code Here] - Print the number of misclassified samples in the test setmlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)print(np.sum(y_test != y_pred))mlp.train(X_train_scaled, y_train)y_pred = mlp.classify(X_test_scaled)print((y_test - y_pred).count_nonzero())mlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)misclassified = np.where(y_test != y_pred, 1, 0)print(misclassified.sum())mlp.train(X_train_scaled, y_train)y_pred = mlp.test(X_test_scaled)print(np.count_nonzero(y_test == y_pred))

rnf=RandomForestClassifier(max_depth=4)rnf.fit(X_train,y_train)​# Building our out-of sample predictionsy_test_pred=rnf.predict(X_test)​# We can measure the out-of-sample accuracyos_accuracy=metrics.accuracy_score(y_test,y_test_pred)print('Out-of-Sample Accuracy:',round(os_accuracy,5))​# We can measure the out-of-Sample simulated investment performance os_prod=pd.Series(y_test_pred.astype('int64'),index=X_test.index).rename('Winner')retl,dial=ap.ml.analysis(os_pred.prices)dialOut-of-Sample Accuracy: 0.52622---------------------------------------------------------------------------AttributeError Traceback (most recent call last)Cell In[17], line 13 11 # We can measure the out-of-Sample simulated investment performance 12 os_prod=pd.Series(y_test_pred.astype('int64'),index=X_test.index).rename('Winner')---> 13 retl,dial=ap.ml.analysis(os_pred.prices) 14 dialAttributeError: module 'apmodule' has no attribute 'ml'

Classification models accuracy is measured byallmean square erroraccuracy scoreroot mean square error