Description Usage Arguments Details Value Examples

Get model performance metrics

1 2 3 4 5 6 7 |

`x` |
Object to be evaluted |

`...` |
Not used |

`na.rm` |
Logical. If FALSE (default) performance metrics will be NA if any rows are missing an outcome value. If TRUE, performance will be evaluted on the rows that have an outcome value. Only used when evaluating a prediction data frame. |

`all_models` |
Logical. If FALSE (default), a numeric vector giving performance metrics for the best-performing model is returned. If TRUE, a data frame with performance metrics for all trained models is returned. Only used when evaluating a model_list. |

This function gets model performance from a model_list object that
comes from `machine_learn`

, `tune_models`

,
`flash_models`

, or a data frame of predictions from
`predict.model_list`

. For the latter, the data passed to
`predict.model_list`

must contain observed outcomes. If you have
predictions and outcomes in a different format, see
`evaluate_classification`

or `evaluate_regression`

instead.

You may notice that `evaluate(models)`

and
`evaluate(predict(models))`

return slightly different performance
metrics, even though they are being calculated on the same (out-of-fold)
predictions. This is because metrics in training (returned from
`evaluate(models)`

) are calculated within each cross-validation fold
and then averaged, while metrics calculated on the prediction data frame
(`evaluate(predict(models))`

) are calculated once on all observations.

Either a numeric vector or a data frame depending on the value of all_models

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
models <- machine_learn(pima_diabetes[1:40, ],
patient_id,
outcome = diabetes,
models = c("XGB", "RF"),
tune = FALSE,
n_folds = 3)
# By default, evaluate returns performance of only the best model
evaluate(models)
# Set all_models = TRUE to see the performance of all trained models
evaluate(models, all_models = TRUE)
# Can also get performance on a test dataset
predictions <- predict(models, newdata = pima_diabetes[41:50, ])
evaluate(predictions)
``` |

healthcareai documentation built on Sept. 2, 2018, 1:03 a.m.

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