class_docstr: Abstract strategy class for time series supervised learning.
  
  Accepts a low-level estimator to perform a given task.
  
  Implements predict and internal fit methods for time series regression and
  classification.
docstr: Fit estimator - inner logic.
  
  Parameters
  ----------
  data : pandas.DataFrame
      Dataframe with feature and target variables as specified in task.
  
  Returns
  -------
  self : an instance of self
docstr_tok: ["Fit","estimator","-","inner","logic",".","Parameters","--","--","--","--","--","data",":","pandas.DataFrame","Dataframe","with","feature","and","target","variables","as","specified","in","task",".","Returns","--","--","--","-","self",":","an","instance","of","self"]
code: def _fit(self, data):
      """Fit estimator - inner logic.
  
      Parameters
      ----------
      data : pandas.DataFrame
          Dataframe with feature and target variables as specified in task.
  
      Returns
      -------
      self : an instance of self
      """
      # select features and target
      X = data[self._task.features]
      y = data[self._task.target]
  
      # fit the estimator
      return self.estimator.fit(X, y)
  
imports: import pandas
  from joblib import dump, load
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.pipeline import Pipeline
  from sktime.base import BaseEstimator
  from sktime.classification.base import BaseClassifier
  from sktime.forecasting.base._sktime import BaseForecaster
  from sktime.regression.base import BaseRegressor
label_desc: Use image node_id 1 for calling the BaseSupervisedLearningStrategy obj's underlying member method code with example usage: obj._fit(data) and returns: self