error_t RegressionForest<FeatT, LabT, SplitT, SplFitterT>::test_error(const feature_mtx<FeatT> & features,
const label_mtx<LabT> & ground_truth_labels) const {
datapoint_idx_t num_datapoints = features.rows();
// Check matrices are right size
if (!feature_mtx_correct_shape(features, num_datapoints)) {
} else if (!label_mtx_correct_shape(ground_truth_labels, num_datapoints)) {
throw std::invalid_argument("ground_truth_labels is wrong shape");
}
// Create output array for labels
label_mtx<LabT> predicted_labels(num_datapoints, forest_stats.label_dimensions);
// Do the prediction
predict(features, &predicted_labels);
return mean_squared_error(ground_truth_labels, predicted_labels);
}
metrics_accumulator::operator metrics() const
{
return {mean_absolute_error(), median_absolute_error(),
mean_squared_error(), r2_score()};
}
