e: Calculate error between actual and forecast.

In scorer: Quickly Score Models in Data Science and Machine Learning

Description

e takes actual and forecast numeric vectors and returns a numeric vector where forecast is subtracted from the actual.

Usage

e(y_true = NULL, y_pred = NULL, actual = NULL, forecast = NULL)

Arguments

y_true	Ground truth (correct) target values.
y_pred	Estimated target values.
actual	a numeric vector of actuals.
forecast	a numeric vector of forecasts.

Value

a numeric vector of errors.

Examples

# Examples
e(1:100, 100:1)

Example output

  [1] -99 -97 -95 -93 -91 -89 -87 -85 -83 -81 -79 -77 -75 -73 -71 -69 -67 -65
 [19] -63 -61 -59 -57 -55 -53 -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29
 [37] -27 -25 -23 -21 -19 -17 -15 -13 -11  -9  -7  -5  -3  -1   1   3   5   7
 [55]   9  11  13  15  17  19  21  23  25  27  29  31  33  35  37  39  41  43
 [73]  45  47  49  51  53  55  57  59  61  63  65  67  69  71  73  75  77  79
 [91]  81  83  85  87  89  91  93  95  97  99
Warning message:
In e(1:100, 100:1) : 'e' is deprecated. Use 'absolute_error' instead.

scorer documentation built on May 2, 2019, 3:22 a.m.