henchman.plotting.roc_auc¶
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henchman.plotting.
roc_auc
(X, y, model, pos_label=1, prob_col=1, n_splits=1, figargs=None)[source]¶ Plots the reveiver operating characteristic curve. This function creates a fit model and shows the results of the roc curve.
Parameters: - X (pd.DataFrame) – The dataframe on which to create a model.
- y (pd.Series) – The labels for which to create a model.
- pos_label (int) – Which label to check for fpr and tpr. Default is 1.
- prob_col (int) – The columns of the probs dataframe to use.
- n_splits (int) – The number of splits to use in validation.
Example
If the dataframe
X
has a binary classification label y:>>> import henchman.plotting as hplot >>> from sklearn.ensemble import RandomForestClassifier >>> plot = hplot.roc_auc(X, y, RandomForestClassifier()) >>> hplot.show(plot)