Mean squared error

Calculate the mean squared error.

Usage

mse(data, ...)

# S3 method for class 'data.frame'
mse(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

mse_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

Arguments

data

A data.frame containing the columns specified by the truth and estimate arguments.

...

Not currently used.

truth

The column identifier for the true results (that is numeric). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a numeric vector.

estimate

The column identifier for the predicted results (that is also numeric). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a numeric vector.

na_rm

A logical value indicating whether NA values should be stripped before the computation proceeds.

case_weights

The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For mse_vec(), a single numeric value (or NA).

Details

MSE is a metric that should be minimized. The output ranges from 0 to ∞, with 0 indicating perfect predictions.

The formula for MSE is:

$$\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (\text{truth}_i - \text{estimate}_i)^2$$

See also

rmse() for the root mean squared error, which is the square root of MSE and is in the same units as the original data.

All numeric metrics

Other numeric metrics: ccc(), gini_coef(), huber_loss(), huber_loss_pseudo(), iic(), mae(), mape(), mase(), mpe(), msd(), poisson_log_loss(), rmse(), rmse_relative(), rpd(), rpiq(), rsq(), rsq_trad(), smape()

Other accuracy metrics: ccc(), huber_loss(), huber_loss_pseudo(), iic(), mae(), mape(), mase(), mpe(), msd(), poisson_log_loss(), rmse(), rmse_relative(), smape()

Examples

# Supply truth and predictions as bare column names
mse(solubility_test, solubility, prediction)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mse     standard       0.521

library(dplyr)

set.seed(1234)
size <- 100
times <- 10

# create 10 resamples
solubility_resampled <- bind_rows(
  replicate(
    n = times,
    expr = sample_n(solubility_test, size, replace = TRUE),
    simplify = FALSE
  ),
  .id = "resample"
)

# Compute the metric by group
metric_results <- solubility_resampled |>
  group_by(resample) |>
  mse(solubility, prediction)

metric_results
#> # A tibble: 10 × 4
#>    resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 1        mse     standard       0.512
#>  2 10       mse     standard       0.454
#>  3 2        mse     standard       0.513
#>  4 3        mse     standard       0.414
#>  5 4        mse     standard       0.543
#>  6 5        mse     standard       0.456
#>  7 6        mse     standard       0.652
#>  8 7        mse     standard       0.642
#>  9 8        mse     standard       0.404
#> 10 9        mse     standard       0.479

# Resampled mean estimate
metric_results |>
  summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 × 1
#>   avg_estimate
#>          <dbl>
#> 1        0.507