docstr: Returns the coefficient of determination R^2 of the prediction.
  
  The coefficient R^2 is defined as (1 - u/v), where u is the residual
  sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
  sum of squares ((y_true - y_true.mean()) ** 2).sum().
  The best possible score is 1.0 and it can be negative (because the
  model can be arbitrarily worse). A constant model that always
  predicts the expected value of y, disregarding the input features,
  would get a R^2 score of 0.0.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
      Test samples.
  
  y : array-like, shape = (n_samples) or (n_samples, n_outputs)
      True values for X.
  
  Returns
  -------
  score : float
      R^2 of self.predict(X) wrt. y.
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code: def score(self, X, y):
      """Returns the coefficient of determination R^2 of the prediction.
  
      The coefficient R^2 is defined as (1 - u/v), where u is the residual
      sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
      sum of squares ((y_true - y_true.mean()) ** 2).sum().
      The best possible score is 1.0 and it can be negative (because the
      model can be arbitrarily worse). A constant model that always
      predicts the expected value of y, disregarding the input features,
      would get a R^2 score of 0.0.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
          Test samples.
  
      y : array-like, shape = (n_samples) or (n_samples, n_outputs)
          True values for X.
  
      Returns
      -------
      score : float
          R^2 of self.predict(X) wrt. y.
      """
      return r2_score(y, self.predict(X))
  
imports: import textwrap
  from dask_glm import algorithms, families
  from dask_glm.utils import accuracy_score, add_intercept, dot, exp, poisson_deviance, sigmoid
  from sklearn.base import BaseEstimator
  from ..metrics import r2_score
  from ..utils import check_array
  from .utils import lr_prob_stack
label_desc: Use image node_id 3 for calling the LinearRegression obj's underlying member method code with example usage: obj.score(X, y) and returns: r2_score