I was solving this problem, got answer too. But it seems not correct

1.

Question 1

In the previous Matlab grader problems, we have created our own classifier method based on fitting PCA models to different classes. Now let's try it out on a new dataset!

The humanactivity dataset contains thousands of samples of a known-class, 60 feature dataset. The 'class' is 1 of 5 possible human activities: sitting, walking, dancing, running, and standing. The 'features' are features derived from acceleration measurements from the accelerometer sensor of a smartphone located in the participant's pocket taken during the course of the recorded activity. If interested, you can find more information about this dataset

here

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Your goal is to create a classifier program that could be run on a smartphone to classify a person's activity from sensor measurements.

Load the humanactivity dataset into MATLAB and divide it into a training and testing set using the following code.

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load humact.matD_train = feat(1:2:end,:);class_train = actid(1:2:end);D_test = feat(2:2:end,:);class_test = actid(2:2:end);

Use your my_fitpca function create a classification model with the training set.

Use your classification model with your my_predictpca function to estimate classifications on the testing set

What is your classification accuracy on the testing set (percent correct), accurate to 2 decimal places)?

humact.MAT File

Code used:

load humact.mat

D_train = feat(1:2:end,:);

class_train = actid(1:2:end);

D_test = feat(2:2:end,:);

class_test = actid(2:2:end);

% Train the PCA model using my_fitpca function (assuming you have implemented it)

pca_model = my_fitpca(D_train, class_train);

% Use the PCA model to predict the class labels for the testing set (assuming you have implemented my_predictpca function)

predicted_labels = my_predictpca(pca_model, D_test);

% Calculate the classification accuracy

accuracy = 100 * sum(predicted_labels == class_test) / length(class_test);

accuracy = round(accuracy, 2); % Round to 2 decimal places

accuracy

0 / 1 point

19.59

Incorrect

Percent correct can be found my computing 100 times the total number of cases for which the my_predictpca output equals class_test, divided by the total number of entries (length) of class_test.