class_docstr: Model that draws predictions uniformly at random from the list of classes in y.
class_docstr_tok: ["Model","that","draws","predictions","uniformly","at","random","from","the","list","of","classes","in","y","."]
docstr: Predict proba for given X.
docstr_tok: ["Predict","proba","for","given","X","."]
code: def predict_proba(self, X):  # pylint: disable=invalid-name
      """Predict proba for given X."""
      classes = sorted(self.unique_labels.tolist())
      predictions = self.predict(X)
      return create_proba_result(predictions, classes)
  
imports: import numpy
label_desc: Use image node_id 4 for calling the ClassificationUniformModel obj's underlying member method code with example usage: obj.predict_proba(X) and returns: create_proba_result