Adding PCA dimension reduction produces better Test Accuracy
x_train = x_train.reshape(-1, 784)
x_test = x_test.reshape(-1, 784)
After this code rows:
pca = PCA(n_components= 60, random_state= 0 )
x_train = pca.fit_transform(x_train)
x_test = pca.transform(x_test)
The resulting is Test accuracy: 0.9398
Adding PCA dimension reduction produces better Test Accuracy
x_train = x_train.reshape(-1, 784)x_test = x_test.reshape(-1, 784)After this code rows:
pca = PCA(n_components= 60, random_state= 0 )x_train = pca.fit_transform(x_train)x_test = pca.transform(x_test)The resulting is
Test accuracy: 0.9398