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.
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docstr: :param stop_after: Maximum amount of seconds it should fit for, currently ignored
  :param target_encoder: The encoder which will be used to decode the target
  :param dtype_dict: A map of feature names and their data types
  :param target: Name of the target column
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code: def __init__(
      self,
      stop_after: float,
      target_encoder: BaseEncoder,
      dtype_dict: dict,
      target: str,
  ):
      """
      :param stop_after: Maximum amount of seconds it should fit for, currently ignored
      :param target_encoder: The encoder which will be used to decode the target
      :param dtype_dict: A map of feature names and their data types
      :param target: Name of the target column
      """  # noqa
      super().__init__(stop_after)
      self.target_encoder = target_encoder
      self.target_dtype = dtype_dict[target]
      self.dtype_dict = dtype_dict
      self.supports_proba = self.target_dtype in [
          dtype.binary,
          dtype.categorical,
      ]
      self.label_map = {}
      self.stable = False
  
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 1 to create a new Regression object from inherited base classes: BaseMixer with example: obj = Regression(stop_after, target_encoder, dtype_dict, target)