class_docstr: The `Regression` mixer inherits from scikit-learn's `Ridge` class
  (https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html)
  
  This class performs Ordinary Least-squares Regression (OLS) under the hood; 
  this means it fits a set of coefficients (w_1, w_2, ..., w_N) for an N-length feature vector, that minimize the difference
  between the predicted target value and the observed true value.
  
  This mixer intakes featurized (encoded) data to predict the target. It deploys if the target data-type is considered numerical/integer.
class_docstr_tok: ["The","`","Regression","`","mixer","inherits","from","scikit-learn","'s","`","Ridge","`","class","(","https",":","\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.Ridge.html",")","This","class","performs","Ordinary","Least-squares","Regression","(","OLS",")","under","the","hood",";","this","means","it","fits","a","set","of","coefficients","(","w_1",",","w_2",",","...",",","w_N",")","for","an","N-length","feature","vector",",","that","minimize","the","difference","between","the","predicted","target","value","and","the","observed","true","value",".","This","mixer","intakes","featurized","(","encoded",")","data","to","predict","the","target",".","It","deploys","if","the","target","data-type","is","considered","numerical\/integer","."]
docstr: Make predictions based on datasource with the same features as the ones used for fitting
  
  :param ds: Predictions are generate from it
  :param arg: Any additional arguments used in predicting
  
  :returns: A dataframe cotaining the decoded predictions and (depending on the args) additional information such as the probabilites for each target class
docstr_tok: ["Make","predictions","based","on","datasource","with","the","same","features","as","the","ones","used","for","fitting",":","param","ds",":","Predictions","are","generate","from","it",":","param","arg",":","Any","additional","arguments","used","in","predicting",":","returns",":","A","dataframe","cotaining","the","decoded","predictions","and","(","depending","on","the","args",")","additional","information","such","as","the","probabilites","for","each","target","class"]
code: def __call__(
      self,
      ds: EncodedDs,
      args: PredictionArguments = PredictionArguments(),
  ) -> pd.DataFrame:
      """
      Make predictions based on datasource with the same features as the ones used for fitting
  
      :param ds: Predictions are generate from it
      :param arg: Any additional arguments used in predicting
  
      :returns: A dataframe cotaining the decoded predictions and (depending on the args) additional information such as the probabilites for each target class
      """  # noqa
      X = ds.get_encoded_data(include_target=False)
      Yh = self.model.predict(X)
  
      decoded_predictions = self.target_encoder.decode(torch.Tensor(Yh))
  
      ydf = pd.DataFrame({"prediction": decoded_predictions})
  
      if args.predict_proba and self.label_map:
          raw_predictions = softmax(Yh.squeeze(), axis=1)
          for idx, label in enumerate(
              self.target_encoder.rev_map.values()
          ):
              ydf[f"__mdb_proba_{label}"] = raw_predictions[:, idx]
  
      return ydf
  
file_path: lightwood/lightwood/mixer/regression.py
imports: from typing import Optional
  import torch
  import pandas
  from scipy.special import softmax
  from sklearn.linear_model import Ridge
  from lightwood.helpers.log import log
  from type_infer.dtype import dtype
  from lightwood.mixer import BaseMixer
  from lightwood.encoder.base import BaseEncoder
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import ConcatedEncodedDs, EncodedDs
label_desc: Use image node_id 4 for calling the Regression obj's underlying member method code with example usage: obj.__call__(ds, args) and returns: ydf