# Classification Function The classify() function allows the user to combine the task of random projection based dimension reduction and classification within a single function. The dimension of the training data and test data was reduced by the value returned from the dimension() method. Then the projection matrix was generated using form_matrix() function based on the input paramater "projection".Then the training data and test data was projected into the low dimensional space by multiplying with the projection matrix. At last the reduced matrix was given to the classifier. The confusion matrix is the output of the classifier where we can calculate the performance of the classifier. ```r classify( train_data, test_data, train_label, test_label, eps = 0.1, projection = "gaussian", classifier = "knn" ) ``` ## Arguments - `train_data`: - Training data of either matrix or data frame - `test_data`: - Test data of either matrix or data frame - `train_label`: - Training label of either vector or data frame - `test_label`: - Test label of either vector or data frame - `eps`: - Epsilon with default 0.1 - `projection`: - projection function with default "gaussian" - `classifier`: - classifier with default "knn" ## Returns Confusion Matrix ## Details The parameters train_data,test_data,train_label and test_label are mandatory arguments. The eps is the error tolerance paramater. The value of eps must be $0.0

classify function