Scorers#

Different available choices for the scorer parameter are shown below. scorer is accepted by ProblemSpec and ParamSearch. The str indicator for each scorer is represented by the sub-heading (within “”) The available scorers are further broken down by which can work with different problem_types. Additionally, a link to the original models documentation is shown.

binary#

“accuracy”#

Base Func Documentation: sklearn.metrics.accuracy_score()

“roc_auc”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr_weighted”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo_weighted”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“balanced_accuracy”#

“average_precision”#

“neg_log_loss”#

Base Func Documentation: sklearn.metrics.log_loss()

“neg_brier_score”#

Base Func Documentation: sklearn.metrics.brier_score_loss()

“precision”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_macro”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_micro”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_samples”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_weighted”#

Base Func Documentation: sklearn.metrics.precision_score()

“recall”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_macro”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_micro”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_samples”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_weighted”#

Base Func Documentation: sklearn.metrics.recall_score()

“f1”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_macro”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_micro”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_samples”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_weighted”#

Base Func Documentation: sklearn.metrics.f1_score()

“jaccard”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_macro”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_micro”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_samples”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_weighted”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“neg_hamming”#

Base Func Documentation: sklearn.metrics.hamming_loss()

“matthews”#

Base Func Documentation: sklearn.metrics.matthews_corrcoef()

“default”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

regression#

“explained_variance”#

“explained_variance score”#

“r2”#

Base Func Documentation: sklearn.metrics.r2_score()

“max_error”#

Base Func Documentation: sklearn.metrics.max_error()

“neg_median_absolute_error”#

Base Func Documentation: sklearn.metrics.median_absolute_error()

“median_absolute_error”#

Base Func Documentation: sklearn.metrics.median_absolute_error()

“neg_mean_absolute_error”#

Base Func Documentation: sklearn.metrics.mean_absolute_error()

“mean_absolute_error”#

Base Func Documentation: sklearn.metrics.mean_absolute_error()

“neg_mean_squared_error”#

Base Func Documentation: sklearn.metrics.mean_squared_error()

“mean_squared_error”#

Base Func Documentation: sklearn.metrics.mean_squared_error()

“neg_mean_squared_log_error”#

“mean_squared_log_error”#

“neg_root_mean_squared_error”#

Base Func Documentation: sklearn.metrics.mean_squared_error()

“root_mean_squared_error”#

Base Func Documentation: sklearn.metrics.mean_squared_error()

“neg_mean_poisson_deviance”#

Base Func Documentation: sklearn.metrics.mean_poisson_deviance()

“mean_poisson_deviance”#

Base Func Documentation: sklearn.metrics.mean_poisson_deviance()

“neg_mean_gamma_deviance”#

Base Func Documentation: sklearn.metrics.mean_gamma_deviance()

“mean_gamma_deviance”#

Base Func Documentation: sklearn.metrics.mean_gamma_deviance()

“default”#

Base Func Documentation: sklearn.metrics.r2_score()

categorical#

“accuracy”#

Base Func Documentation: sklearn.metrics.accuracy_score()

“roc_auc”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovr_weighted”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“roc_auc_ovo_weighted”#

Base Func Documentation: sklearn.metrics.roc_auc_score()

“balanced_accuracy”#

“average_precision”#

“neg_log_loss”#

Base Func Documentation: sklearn.metrics.log_loss()

“neg_brier_score”#

Base Func Documentation: sklearn.metrics.brier_score_loss()

“precision”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_macro”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_micro”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_samples”#

Base Func Documentation: sklearn.metrics.precision_score()

“precision_weighted”#

Base Func Documentation: sklearn.metrics.precision_score()

“recall”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_macro”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_micro”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_samples”#

Base Func Documentation: sklearn.metrics.recall_score()

“recall_weighted”#

Base Func Documentation: sklearn.metrics.recall_score()

“f1”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_macro”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_micro”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_samples”#

Base Func Documentation: sklearn.metrics.f1_score()

“f1_weighted”#

Base Func Documentation: sklearn.metrics.f1_score()

“jaccard”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_macro”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_micro”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_samples”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“jaccard_weighted”#

Base Func Documentation: sklearn.metrics.jaccard_score()

“neg_hamming”#

Base Func Documentation: sklearn.metrics.hamming_loss()

“matthews”#

Base Func Documentation: sklearn.metrics.matthews_corrcoef()

“default”#

Base Func Documentation: sklearn.metrics.roc_auc_score()