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

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))

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

You want to classify images of dogs from cats. You have collected 2,000 images of dogs and 2,000 images of cats. How would you split the data effectively into a training set and a validation set?Group of answer choicesYou should split by light or dark fur color.You should split by whether the image contains a cat or dog.You should split by high- or low-quality images.You should split uniformly at random.

If we wanted to identify the cat in the image below, what kind of algorithm will we be working with?1 pointA classifierA detectorCV studioA Sliding window

Question 4You are working to create an object detection system, like the ones described in the lectures, to locate cats in a room. To have more data with which to train, you search on the internet and find a large number of cat photos.Which of the following is true about the system?1 point