The function DecisionTree Clasifier taken various input mentioned below
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Click Here to see main data file dataLRS.csvmax_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.
#If you are using jupyter notebook then use shift + TAB for help with function
Library Used: import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn import tree