class_docstr: Accessor to ``sklearn.gaussian_process``.
code: def _predict(cls, df, estimator, *args, **kwargs):
      data = df.data.values
      eval_MSE = kwargs.get("eval_MSE", False)
      if eval_MSE:
          y, MSE = estimator.predict(data, *args, **kwargs)
          if y.ndim == 1:
              y = df._constructor_sliced(y, index=df.index)
              MSE = df._constructor_sliced(MSE, index=df.index)
          else:
              y = df._constructor(y, index=df.index)
              MSE = df._constructor(MSE, index=df.index)
          return y, MSE
      else:
          y = estimator.predict(data, *args, **kwargs)
          if y.ndim == 1:
              y = df._constructor_sliced(y, index=df.index)
          else:
              y = df._constructor(y, index=df.index)
          return y
  
file_path: pandas-ml/pandas_ml/skaccessors/gaussian_process.py
imports: from pandas_ml.core.accessor import _AccessorMethods, _attach_methods, _wrap_data_func
label_desc: Use image node_id 3 for calling the GaussianProcessMethods obj's underlying member method code with example usage: obj._predict(cls, df, estimator) and returns: y,  MSE, y