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# Returns a subset of the original data with the selected features
subset = Feature_Selector.Subset()
subset.head()

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# Returns Boxplot of features
Feature_Selector.plot(which_features='all', 
                      X_size=8, figsize=(12,8),
                      y_scale='log')

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# no model selected default is Random Forest, if classification is True it is a Classification problem
Feature_Selector = BorutaShap(importance_measure='shap',
                              classification=False)

Feature_Selector.fit(X=X, y=y, n_trials=100, random_state=0)

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from BorutaShap import BorutaShap, load_data
  
X, y = load_data(data_type='regression')
X.head()

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import numpy as np
import pandas as pd

class Node:
    '''
    This class defines a node which creates a tree structure by recursively calling itself 
    whilst checking a number of ending parameters such as depth and min_leaf. It uses an exact greedy method
    to exhaustively scan every possible split point. Algorithm is based on Frieman's 2001 Gradient Boosting Machines
    
    Input

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import numpy as np
import pandas as pd
from math import e

class Node:
    
    '''
    A node object that is recursivly called within itslef to construct a regression tree. Based on Tianqi Chen's XGBoost 
    the internal gain used to find the optimal split value uses both the gradient and hessian. Also a weighted quantlie sketch 
    and optimal leaf values all follow Chen's description in "XGBoost: A Scalable Tree Boosting System" the only thing not 

Ekeany

import numpy as np
import pandas as pd
from math import e

class Node:
    '''
    This class defines a node which creates a tree structure by recursively calling itself 
    whilst checking a number of ending parameters such as depth and min_leaf. It uses an exact greedy method
    to exhaustively scan every possible split point. The gain metric of choice is conservation of varience.
    This is a Naive solution and does not comapre to Frieman's 2001 Gradient Boosting Machines