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add_class_node(self: pysmile.learning.Validator, node_handle: int) -> None
add_class_node(self: pysmile.learning.Validator, node_id: str) -> None

Adds class node to validator

get_accuracy(self: pysmile.learning.Validator, node_handle: int, outcome_index: int) -> float
get_accuracy(self: pysmile.learning.Validator, node_id: str, outcome_index: int) -> float
get_accuracy(self: pysmile.learning.Validator, node_handle: int, outcome_id: str) -> float
get_accuracy(self: pysmile.learning.Validator, node_id: str, outcome_id: str) -> float

Gets classification accuracy

get_calibration_curve_b(self: pysmile.learning.Validator, node_handle: int, outcome_index: int, bin_count: int) -> pysmile.learning.Curve
get_calibration_curve_b(self: pysmile.learning.Validator, node_id: str, outcome_index: int, bin_count: int) -> pysmile.learning.Curve
get_calibration_curve_b(self: pysmile.learning.Validator, node_handle: int, outcome_id: str, bin_count: int) -> pysmile.learning.Curve
get_calibration_curve_b(self: pysmile.learning.Validator, node_id: str, outcome_id: str, bin_count: int) -> pysmile.learning.Curve

Returns the calibration curve by binning for the specified node, outcome and bin count

get_calibration_curve_ma(self: pysmile.learning.Validator, node_handle: int, outcome_index: int, window_size: int) -> pysmile.learning.Curve
get_calibration_curve_ma(self: pysmile.learning.Validator, node_id: str, outcome_index: int, window_size: int) -> pysmile.learning.Curve
get_calibration_curve_ma(self: pysmile.learning.Validator, node_handle: int, outcome_id: str, window_size: int) -> pysmile.learning.Curve
get_calibration_curve_ma(self: pysmile.learning.Validator, node_id: str, outcome_id: str, window_size: int) -> pysmile.learning.Curve

Returns the calibration curve by moving average for the specified node, outcome and window size

get_confusion_matrix(self: pysmile.learning.Validator, node_handle: int) -> List[List[int]]
get_confusion_matrix(self: pysmile.learning.Validator, node_id: str) -> List[List[int]]

Gets confusion matrix

get_result_data_set(self: pysmile.learning.Validator) -> pysmile.learning.DataSet

Gets result dataset after validation

get_roc(self: pysmile.learning.Validator, node_handle: int, outcome_index: int) -> pysmile.learning.Curve
get_roc(self: pysmile.learning.Validator, node_id: str, outcome_index: int) -> pysmile.learning.Curve
get_roc(self: pysmile.learning.Validator, node_handle: int, outcome_id: str) -> pysmile.learning.Curve
get_roc(self: pysmile.learning.Validator, node_id: str, outcome_id: str) -> pysmile.learning.Curve

Returns the ROC curve for the specified node and outcome

k_fold(self: pysmile.learning.Validator, em: pysmile.learning.EM, fold_count: int, folding_rand_seed: int) -> None
k_fold(self: pysmile.learning.Validator, em: pysmile.learning.EM, fold_count: int) -> None

Performs k-fold cross-validation

leave_one_out(self: pysmile.learning.Validator, em: pysmile.learning.EM) -> None

Performs leave-one-out cross-validation

test(self: pysmile.learning.Validator) -> None

Tests the model on a dataset