When classifying images into tiger and lions, which image classification algorithm finds the best plane that divides a dataset into two classes?1 pointK-Nearest NeighboursLogistic RegressionSupport Vector MachinesK-Means

In machine learning, which algorithm is known for creating an optimal hyperplane to classify data points?Review LaterAdaBoost ClassifierRandom ForestSupport Vector Machine (SVM)K-Nearest Neighbors

You are fine-tuning a support vector machine (SVM) classifier to categorise images based on their content. The dataset consists of various animal images, and you suspect that different kernel functions might yield better classification accuracy. You decide to test which SVM kernel—linear or radial basis function (RBF)—works best for your specific dataset. Below is your initial code setup:from sklearn.svm import SVCfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score# Load a dataset of digit imagesdigits = load_digits()X = digits.datay = digits.target# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise two SVM classifiers, one with a linear kernel and another with an RBF kernelsvm_linear = SVC(kernel='linear')svm_rbf = SVC(kernel='rbf')# [Your Code Here] - Train both classifiers on the training data# [Your Code Here] - Predict the test set results with both classifiers# [Your Code Here] - Calculate and print the accuracy scores for both classifiersWhich of the following options correctly completes the task of training both SVM classifiers, predicting the test set results, and calculating the accuracy for eachsvm_linear.train(X_train, y_train)svm_rbf.train(X_train, y_train)y_pred_linear = svm_linear.classify(X_test)y_pred_rbf = svm_rbf.classify(X_test)print("Linear Kernel Accuracy:", accuracy_score(y_test, y_pred_linear))print("RBF Kernel Accuracy:", accuracy_score(y_test, y_pred_rbf))svm_linear.fit(X_train, y_train)svm_rbf.fit(X_train, y_train)y_pred_linear = svm_linear.predict(X_test)y_pred_rbf = svm_rbf.predict(X_test)print("Linear Accuracy:", accuracy_score(y_test, y_pred_linear))print("RBF Accuracy:", accuracy_score(y_test, y_pred_rbf))svm_linear.fit(X_train, y_train)y_pred_linear = svm_linear.predict(X_train)svm_rbf.fit(X_train, y_train)y_pred_rbf = svm_rbf.predict(X_train)print("Accuracy with Linear Kernel:", accuracy_score(y_train, y_pred_linear))print("Accuracy with RBF Kernel:", accuracy_score(y_train, y_pred_rbf))svm_linear.fit(X_train, y_train)y_pred_linear = svm_linear.predict(X_test)svm_rbf.fit(X_train, y_train)y_pred_rbf = svm_rbf.predict(X_test)print("Accuracy with Linear Kernel:", accuracy_score(y_test, y_pred_linear))print("Accuracy with RBF Kernel:", accuracy_score(y_test, y_pred_rbf))

Which of the following algorithms is not primarily used for classification tasks?Decision TreesRandom ForestsAprioriSupport Vector Machines

What are some common algorithms used in classification?

Explain the steps in SVM algorithm to classify the images of cats and dogs