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Computes R2 score.

Source: R/metrics.R
metric_r2_score.Rd

Formula:

sum_squares_residuals <- sum((y_true - y_pred) ** 2)
sum_squares <- sum((y_true - mean(y_true)) ** 2)
R2 <- 1 - sum_squares_residuals / sum_squares

This is also called the coefficient of determination.

It indicates how close the fitted regression line is to ground-truth data.

  • The highest score possible is 1.0. It indicates that the predictors perfectly accounts for variation in the target.

  • A score of 0.0 indicates that the predictors do not account for variation in the target.

  • It can also be negative if the model is worse than random.

This metric can also compute the "Adjusted R2" score.

Usage

metric_r2_score(
  ...,
  class_aggregation = "uniform_average",
  num_regressors = 0L,
  name = "r2_score",
  dtype = NULL
)

Arguments

...

For forward/backward compatability.

class_aggregation

Specifies how to aggregate scores corresponding to different output classes (or target dimensions), i.e. different dimensions on the last axis of the predictions. Equivalent to multioutput argument in Scikit-Learn. Should be one of NULL (no aggregation), "uniform_average", "variance_weighted_average".

num_regressors

Number of independent regressors used ("Adjusted R2" score). 0 is the standard R2 score. Defaults to 0.

name

Optional. string name of the metric instance.

dtype

Optional. data type of the metric result.

Value

a Metric instance is returned. The Metric instance can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.

Examples

y_true <- rbind(1, 4, 3)
y_pred <- rbind(2, 4, 4)
metric <- metric_r2_score()
metric$update_state(y_true, y_pred)
metric$result()

## tf.Tensor(0.57142854, shape=(), dtype=float32)

See also

Other regression metrics:
metric_cosine_similarity()
metric_log_cosh_error()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_root_mean_squared_error()

Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sensitivity_at_specificity()
metric_sparse_categorical_accuracy()
metric_sparse_categorical_crossentropy()
metric_sparse_top_k_categorical_accuracy()
metric_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()