class_docstr: A `RandomRegressor` makes random predictions only based on the `y` value that is seen.
  
  The goal is that such a regressor can be used for benchmarking. It _should be_ easily beatable.
  
  Parameters
  ----------
  strategy : Literal["uniform", "normal"], default="uniform"
      How we want to select random values, either "uniform" or "normal"
  random_state : int | None, default=None
      The seed value used for the random number generator.
  
  Attributes
  ----------
  min_ : float
      The minimum value of `y` seen during `fit`.
  max_ : float
      The maximum value of `y` seen during `fit`.
  mu_ : float
      The mean value of `y` seen during `fit`.
  sigma_ : float
      The standard deviation of `y` seen during `fit`.
  n_features_in_ : int
      The number of features seen during `fit`.
  dim_ : int
      Deprecated, please use `n_features_in_` instead.
  
  Examples
  --------
  ```py
  from sklego.dummy import RandomRegressor
  from sklearn.datasets import make_regression
  
  X, y = make_regression(n_samples=10, n_features=2, random_state=42)
  
  RandomRegressor(strategy="uniform", random_state=123).fit(X, y).predict(X).round(2)
  # array([ 57.63, -66.05, -83.92,  13.88,  64.56, -24.77, 143.33,  54.12,
  #     -7.34, -34.11])
  
  RandomRegressor(strategy="normal", random_state=123).fit(X, y).predict(X).round(2)
  # array([-128.45,   78.05,    7.23, -170.15,  -78.18,  142.9 , -261.39,
  #     -63.34,  104.68, -106.75])
  ```
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docstr: Predict new data by generating random guesses following the given `strategy` based on the `y` statistics seen
  during `fit`.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The data to predict.
  
  Returns
  -------
  array-like of shape (n_samples,)
      The predicted data.
docstr_tok: ["Predict","new","data","by","generating","random","guesses","following","the","given","`","strategy","`","based","on","the","`","y","`","statistics","seen","during","`","fit","`",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","The","data","to","predict",".","Returns","--","--","--","-","array-like","of","shape","(","n_samples",",",")","The","predicted","data","."]
code: def predict(self, X):
      """Predict new data by generating random guesses following the given `strategy` based on the `y` statistics seen
      during `fit`.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          The data to predict.
  
      Returns
      -------
      array-like of shape (n_samples,)
          The predicted data.
      """
      rs = check_random_state(self.random_state)
      check_is_fitted(
          self, ["n_features_in_", "min_", "max_", "mu_", "sigma_"]
      )
  
      X = check_array(X, estimator=self, dtype=FLOAT_DTYPES)
      if X.shape[1] != self.n_features_in_:
          raise ValueError(
              f"Unexpected input dimension {X.shape[1]}, expected {self.dim_}"
          )
  
      if self.strategy == "normal":
          return rs.normal(self.mu_, self.sigma_, X.shape[0])
      if self.strategy == "uniform":
          return rs.uniform(self.min_, self.max_, X.shape[0])
  
file_path: scikit-lego/sklego/dummy.py
imports: from warnings import warn
  import numpy
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.utils import check_X_y
  from sklearn.utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, check_random_state
label_desc: Use image node_id 3 for calling the RandomRegressor obj's underlying member method code with example usage: obj.predict(X) and returns: rs, rs