Java: Data Science Made Easy

MSE

Mean Squared Error (MSE) is the sum of squared differences between the actual and predicted values. It is quite easy to compute it in Java:

double[] actual, predicted;  int n = actual.length; double sum = 0.0; for (int i = 0; i < n; i++) {     diff = actual[i] - predicted[i];     sum = sum + diff * diff; } double mse = sum / n;

Typically, the value of MSE is hard to interpret, which is why we often take a square root of MSE; this is called Root Mean Squared Error (RMSE). It is easier to interpret because it is in the same units as the target variable.

double rmse = Math.sqrt(mse);