ertk.classification.dataset_train_val_test
- ertk.classification.dataset_train_val_test(clf, dataset: Dataset, train_idx: Sequence[int] | ndarray, valid_idx: Sequence[int] | ndarray, test_idx: Sequence[int] | ndarray | None = None, label: str = 'label', clf_lib: str | None = None, sample_weight: ndarray | None = None, verbose: int = 0, scoring=None, fit_params: Dict[str, Any] = {}) ExperimentResult
Trains a
Classifierinstance on some training data, optionally using validation data, and returns results on given test data.- Parameters:
- clf: class that implements fit() and predict()
The classifier to test.
- dataset: Dataset
The dataset for within-corpus cross-validation.
- clf_lib: str
One of {“sk”, “tf”, “pt”} to select which library-specific cross-validation method to use, since they’re not all quite compatible.
- verbose: bool
Passed to train_val_test().
- scoring: str, list, dict, optional
Scoring metric(s) to use. Can be anything accepted by scikit-learn’s cross_val* methods (i.e. str, list or dict).
- fit_params: dict
Additional parameters passed to the model’s fit() method. This should be used to pass any more specific parameters not covered here.
- Returns:
- df: pandas.DataFrame
A dataframe holding the results from all runs with this model.