mse: Mean Square Error

In mltools: Machine Learning Tools

Description

Calculate Mean-Square Error (Deviation)

For the ith sample, Squared Error is calculated as SE = (prediction - actual)^2. MSE is then mean(squared errors).

Usage

mse(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

preds	A vector of prediction values in [0, 1]
actuals	A vector of actuals values in 0, 1, or FALSE, TRUE
weights	Optional vectors of weights
na.rm	Should (prediction, actual) pairs with at least one NA value be ignored?

Details

Calculate Mean-Square Error (Deviation)

References

https://en.wikipedia.org/wiki/Mean_squared_error

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
mse(preds, actuals)

mltools documentation built on May 2, 2019, 5:22 a.m.