ML.EVAL.REGRESSION.EXPLAINED_VARIANCE_SCORE¶
Returns the explained variance regression score function.
Syntax¶
ML.EVAL.REGRESSION.EXPLAINED_VARIANCE_SCORE(y_true, y_pred, sample_weight, multioutput, force_finite)
Arguments¶
| Name | Type | Default | Description |
|---|---|---|---|
| y_true | object | DataFrame or array object of ground-truth target values. | |
| y_pred | object | DataFrame or array object of predicted target values. | |
| sample_weight | Any | None | Optional DataFrame or array object of per-sample weights. Omit for uniform weights. |
| multioutput | Any | "uniform_average" | How to aggregate scores across multiple output dimensions. One of 'raw_values', 'uniform_average', or 'variance_weighted'. |
| force_finite | Any | TRUE | If TRUE, replace non-finite scores (NaN, -inf) with 1.0 for a perfect fit and 0.0 otherwise. |
Examples¶
Examples coming soon
Working Excel formula examples for this function are not yet written.
See also¶
- ML.EVAL.REGRESSION.D2_ABSOLUTE_ERROR_SCORE
- ML.EVAL.REGRESSION.D2_PINBALL_SCORE
- ML.EVAL.REGRESSION.D2_TWEEDIE_SCORE
- ML.EVAL.REGRESSION.MAX_ERROR
- ML.EVAL.REGRESSION.MEAN_ABSOLUTE_ERROR
- ML.EVAL.REGRESSION.MEAN_ABSOLUTE_PERCENTAGE_ERROR
- ML.EVAL.REGRESSION.MEAN_GAMMA_DEVIANCE
- ML.EVAL.REGRESSION.MEAN_POISSON_DEVIANCE
- ML.EVAL.REGRESSION.MEAN_SQUARED_ERROR
- ML.EVAL.REGRESSION.MEAN_SQUARED_LOG_ERROR
- ML.EVAL.REGRESSION.MEDIAN_ABSOLUTE_ERROR
- ML.EVAL.REGRESSION.R2_SCORE
- ML.EVAL.REGRESSION.ROOT_MEAN_SQUARED_ERROR
- ML.EVAL.REGRESSION.ROOT_MEAN_SQUARED_LOG_ERROR