Explainer
Explain predictions for a fitted model
Explainer
teller.Explainer(obj, n_jobs=None, y_class=0, normalize=False)
Class Explainer: effects of features on the response.
Attributes:
obj: an object;
fitted object containing methods `fit` and `predict`
n_jobs: an integer;
number of jobs for parallel computing
y_class: an integer;
class whose probability has to be explained (for classification only)
normalize: a boolean;
whether the features must be normalized or not (changes the effects)
fit
Explainer.fit(X, y, X_names, method="avg", type_ci="jackknife", scoring=None, level=95, col_inters=None)
Fit the explainer's attribute obj
to training data (X, y).
Args:
X: array-like, shape = [n_samples, n_features];
Training vectors, where n_samples is the number
of samples and n_features is the number of features.
y: array-like, shape = [n_samples, ]; Target values.
X_names: {array-like}, shape = [n_features, ];
Column names (strings) for training vectors.
method: str;
Type of summary requested for effects. Either `avg`
(for average effects), `inters` (for interactions)
or `ci` (for effects including confidence intervals
around them).
type_ci: str;
Type of resampling for `method == 'ci'` (confidence
intervals around effects). Either `jackknife`
bootsrapping or `gaussian` (gaussian white noise with
standard deviation equal to `0.01` applied to the
features).
scoring: str;
measure of errors must be in ("explained_variance",
"neg_mean_absolute_error", "neg_mean_squared_error",
"neg_mean_squared_log_error", "neg_median_absolute_error",
"r2", "rmse") (default: "rmse").
level: int; Level of confidence required for
`method == 'ci'` (in %).
col_inters: str; Name of column for computing interactions.
summary
Explainer.summary()
Summarise results
a method in class Explainer
Args:
None