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: Predict using the given test data.
  
  Parameters
  ----------
  data : a pandas.DataFrame
      Dataframe with feature and target variables as specified in task
      passed to ``fit``.
  
  
  Returns
  -------
  y_pred : pandas.Series
      Returns the series of predicted values.
docstr_tok: ["Predict","using","the","given","test","data",".","Parameters","--","--","--","--","--","data",":","a","pandas.DataFrame","Dataframe","with","feature","and","target","variables","as","specified","in","task","passed","to","``","fit","``",".","Returns","--","--","--","-","y_pred",":","pandas.Series","Returns","the","series","of","predicted","values","."]
code: def predict(self, data):
      """Predict using the given test data.
  
      Parameters
      ----------
      data : a pandas.DataFrame
          Dataframe with feature and target variables as specified in task
          passed to ``fit``.
  
  
      Returns
      -------
      y_pred : pandas.Series
          Returns the series of predicted values.
      """
      # select features
      X = data[self._task.features]
  
      # predict
      return self.estimator.predict(X)
  
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 2 for calling the BaseSupervisedLearningStrategy obj's underlying member method code with example usage: obj.predict(data) and returns: self