multieval: Evaluation of multiple metrics and predictions

In sknifedatar: Swiss Knife of Data

Description

for a set of predictions from different models, evaluate multiple metrics and return the results in a tabular format that makes it easy to compare the predictions.

Usage

multieval(.dataset, .observed, .predictions, .metrics, value_table = FALSE)

Arguments

.dataset	data frame with the predictions, it must have at least the column with the observed data and at least one column that refers to the predictions of a model.
.observed	string with the name of the column that contains the observed data.
.predictions	string or vector of strings the columns where the predictions are stored.
.metrics	metric or set of metrics to be evaluated, the metrics refer to those allowed by the package 'yardstick' from 'tidymodels'.
value_table	TRUE to display disaggregated metrics.

Value

data frame with 4 columns: the evaluation metrics, the estimator used, the value of the metric and the name of the model.

See Also

Crime prediction /multieval

Examples

set.seed(123)
library(yardstick) # métricas

predictions <-
  data.frame(truth = runif(100),
             predict_model_1 = rnorm(100, mean = 1,sd =2),
             predict_model_2 = rnorm(100, mean = 0,sd =2),
             predict_model_3 = rnorm(100, mean = 0,sd =3))

multieval(.dataset = predictions,
          .observed = "truth",
          .predictions = c("predict_model_1","predict_model_2","predict_model_3"),
          .metrics = list(rmse = rmse, rsq = rsq, mae = mae),
          value_table = TRUE)

# Output ----------------------
# A tibble: 9 x 4
# .metric .estimator .estimate model
# <chr>   <chr>          <dbl> <chr>
#   1 mae     standard     1.45    predict_model_1
# 2 mae     standard     1.67    predict_model_2
# 3 mae     standard     2.43    predict_model_3
# 4 rmse    standard     1.78    predict_model_1
# 5 rmse    standard     2.11    predict_model_2
# 6 rmse    standard     3.01    predict_model_3
# 7 rsq     standard     0.00203 predict_model_1
# 8 rsq     standard     0.0158  predict_model_2
# 9 rsq     standard     0.00254 predict_model_3

#$summary_table
# A tibble: 3 x 4
#  model             mae  rmse     rsq
#  <chr>           <dbl> <dbl>   <dbl>
#  1 predict_model_1  1.45  1.78 0.00203
#  2 predict_model_2  1.67  2.11 0.0158
#  3 predict_model_3  2.43  3.01 0.00254

sknifedatar documentation built on June 1, 2021, 9:08 a.m.