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))

Which code snippet can be used to fill in the missing lines of code to train the SVM classifier, predict the test set results, and print the classification report?from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import classification_report# 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 the SVM classifier with a radial basis function kernelsvm_rbf = SVC(kernel='rbf')# Fit the classifier to the training data# [Your Code Here] - Line to add for fitting the model# Predict the test set results# [Your Code Here] - Line to add for making predictions# Generate and print the classification report# [Your Code Here] - Line to add for printing the classification report

Identify which line has Error1. from sklearn.neural_network import MLPClassifier  2. model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200) 3. model.fit(X_train, y_train) 4. predictions = model.predict(X_test, y_test)  1 & 3234

You run the following code to train an MLPClassifier but encounter an error. What could be the issue?  Code :from sklearn.neural_network import MLPClassifier model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200) model.fit(X_train, y_train)Error: ValueError: could not convert string to float: 'setosa'  The target variable y_train contains string labels that need to be encoded.The input features X_train contain string values.The number of iterations is too low.The target variable X_train contains string labels that need to be encoded.

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 .

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?