. Modify the feature extraction code to use only texture features. Rerun the classification and compare the the outcomes.

. Rerun the classification using any combination of features and classifiers.

Find more data and re-train your classifier to classify the misclassified sample, show your new classifier works on the miss classified image

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))

1. Create a train-test split and classify the images using any classifier you have used previously. What is the classifier performance?

Match the following ML Kit ObjectDetector's builder settings with their options.Each answer only matches one item.Single image or stream  Single or multiple object detection  Classification mode—on or off