Description Usage Arguments Examples

returns a list containing a data frame of accuracy values and other cross-validation statistics. In the data frame, accuracy_subset contains accuracy of cross-validation on user-specified features, while accuracy_all contains accuracy of cross-validation on all the available features from in the data frame df. average_accuracy_subset is the average accuracy of n_iter iterations of cross-validation with user-specified features. average_acuracy_all is the average accuracy of n_iter iterations of cross-validation with all the available features. variance_accuracy_subset is the variance of accuracy of n_iter iterations of cross-validation with user-specified features. variance_accuracy_all is the variance of accuracy of n_iter iterations of cross-validation with all the available features.

1 | ```
cross_validate(df, tree, n_iter, split_ratio, method)
``` |

`df` |
data frame on which cross-validation is being performed. |

`tree` |
tree object generated from rpart on df |

`n_iter` |
number of iterations for cross-validation |

`split_ratio` |
train/test split ratio for cross-validation. split_ratio is supposed to be between 0 to 1. |

`method` |
method for decision tree. The default value is 'class' for classification. Set method = 'anova' for regression. |

1 | ```
cross_validate(df, tree, n_iter = 10, split_ratio = 0.8, method = 'class')
``` |

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