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”#
Base Func Documentation:
sklearn.metrics.balanced_accuracy_score()
“average_precision”#
Base Func Documentation:
sklearn.metrics.average_precision_score()
“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”#
Base Func Documentation:
sklearn.metrics.explained_variance_score()
“explained_variance score”#
Base Func Documentation:
sklearn.metrics.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”#
Base Func Documentation:
sklearn.metrics.mean_squared_log_error()
“mean_squared_log_error”#
Base Func Documentation:
sklearn.metrics.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”#
Base Func Documentation:
sklearn.metrics.balanced_accuracy_score()
“average_precision”#
Base Func Documentation:
sklearn.metrics.average_precision_score()
“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()