class_docstr: Linear regression model with a regularization factor which does both variable selection 
  and regularization. Model that tries to balance the fit of the model with respect to the training 
  data and the complexity of the model. A large regularization factor with decreases the variance of 
  the model and do para.
  Parameters:
  -----------
  degree: int
      The degree of the polynomial that the independent variable X will be transformed to.
  reg_factor: float
      The factor that will determine the amount of regularization and feature
      shrinkage. 
  n_iterations: float
      The number of training iterations the algorithm will tune the weights for.
  learning_rate: float
      The step length that will be used when updating the weights.
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code: def __init__(
      self, degree, reg_factor, n_iterations=3000, learning_rate=0.01
  ):
      self.degree = degree
      self.regularization = l1_regularization(alpha=reg_factor)
      super(LassoRegression, self).__init__(n_iterations, learning_rate)
  
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import normalize, polynomial_features
label_desc: Use image node_id 1 to create a new LassoRegression object from inherited base classes: Regression with example: obj = LassoRegression(degree, reg_factor, n_iterations, learning_rate)