Write a Matlab script for the function below, that runs the script 5 times and plots a bar graph
and error bar for the average accuracy. Here is the function:
function perceptron_classifier()
% load data data = load('bill_authentication.txt');
% split data into features and labels
X = data(:,1:4); y = data(:,5);
% normalize features
X = normalize(X);
% split data into training and testing sets
[X_train, y_train, X_test, y_test] = split_data(X, y, 0.8);
% train the perceptron classifier
mdl = fitcsvm(X_train, y_train, 'KernelFunction', 'linear', 'Standardize', true);
% predict on the test set
y_pred = predict(mdl, X_test);
% evaluate the model
accuracy = sum(y_pred == y_test)/length(y_test);
fprintf('Accuracy: %.2f%%\n', accuracy*100);
end
function [X_train, y_train, X_test, y_test] = split_data(X, y, train_ratio)
% randomly shuffle data idx = randperm(size(X,1)); X = X(idx,:);
y = y(idx);
% split data into training and testing sets
split_idx = floor(size(X,1)*train_ratio);
X_train = X(1:split_idx,:);
y_train = y(1:split_idx);
X_test = X(split_idx+1:end,:);
y_test = y(split_idx+1:end);
end.
Plant propagation: Sexual and Asexual propapagation.pptx
Write a Matlab script for the function below, that runs the script 5.pdf
1. Write a Matlab script for the function below, that runs the script 5 times and plots a bar graph
and error bar for the average accuracy. Here is the function:
function perceptron_classifier()
% load data data = load('bill_authentication.txt');
% split data into features and labels
X = data(:,1:4); y = data(:,5);
% normalize features
X = normalize(X);
% split data into training and testing sets
[X_train, y_train, X_test, y_test] = split_data(X, y, 0.8);
% train the perceptron classifier
mdl = fitcsvm(X_train, y_train, 'KernelFunction', 'linear', 'Standardize', true);
% predict on the test set
y_pred = predict(mdl, X_test);
% evaluate the model
accuracy = sum(y_pred == y_test)/length(y_test);
fprintf('Accuracy: %.2f%%n', accuracy*100);
end
function [X_train, y_train, X_test, y_test] = split_data(X, y, train_ratio)
% randomly shuffle data idx = randperm(size(X,1)); X = X(idx,:);
y = y(idx);
% split data into training and testing sets
split_idx = floor(size(X,1)*train_ratio);
X_train = X(1:split_idx,:);
y_train = y(1:split_idx);
X_test = X(split_idx+1:end,:);
y_test = y(split_idx+1:end);
end
![Write a Matlab script for the function below, that runs the script 5 times and plots a bar graph
and error bar for the average accuracy. Here is the function:
function perceptron_classifier()
% load data data = load('bill_authentication.txt');
% split data into features and labels
X = data(:,1:4); y = data(:,5);
% normalize features
X = normalize(X);
% split data into training and testing sets
[X_train, y_train, X_test, y_test] = split_data(X, y, 0.8);
% train the perceptron classifier
mdl = fitcsvm(X_train, y_train, 'KernelFunction', 'linear', 'Standardize', true);
% predict on the test set
y_pred = predict(mdl, X_test);
% evaluate the model
accuracy = sum(y_pred == y_test)/length(y_test);
fprintf('Accuracy: %.2f%%n', accuracy*100);
end
function [X_train, y_train, X_test, y_test] = split_data(X, y, train_ratio)
% randomly shuffle data idx = randperm(size(X,1)); X = X(idx,:);
y = y(idx);
% split data into training and testing sets
split_idx = floor(size(X,1)*train_ratio);
X_train = X(1:split_idx,:);
y_train = y(1:split_idx);
X_test = X(split_idx+1:end,:);
y_test = y(split_idx+1:end);
end](https://image.slidesharecdn.com/writeamatlabscriptforthefunctionbelowthatrunsthescript5-230323065640-a9dbc72b/85/Write-a-Matlab-script-for-the-function-below-that-runs-the-script-5-pdf-1-320.jpg)
