postResample(pred, obs)
defaultSummary(data, lev = NULL, model = NULL)twoClassSummary(data, lev = NULL, model = NULL)
R2(pred, obs, formula = "corr", na.rm = FALSE)
RMSE(pred, obs, na.rm = FALSE)
postResample
is meant to be used with apply
across a matrix. For numeric data
the code checks to see if the standard deviation of either vector is zero. If so, the correlation
between those samples is assigned a value of zero. NA
values are ignored everywhere.Note that many models have more predictors (or parameters) than data points, so the typical mean squared
error denominator (n - p) does not apply. Root mean squared error is calculated using sqrt(mean((pred - obs)^2
.
Also, $R^2$ is calculated wither using as the square of the correlation between the observed and predicted outcomes when form = "corr"
. when form = "traditional"
,
$$R^2 = 1-\frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y}_i)^2}$$
For defaultSummary
is the default function to compute performance metrics in train
. It is a wrapper around postResample
.
twoClassSummary
computes sensitivity, specificity and the area under the ROC curve. To use this function, the classProbs
argument of trainControl
should be TRUE
.
Other functions can be used via the summaryFunction
argument of trainControl
. Custom functions must have the same arguments asdefaultSummary
.
trainControl
predicted <- matrix(rnorm(50), ncol = 5)
observed <- rnorm(10)
apply(predicted, 2, postResample, obs = observed)
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