![]() show ()Ĭ:\Local\anaconda3\envs\MLTech\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. ylabel ( feature_names ) # petal width (cm) plt. xlabel ( feature_names ) # petal length (cm) plt. contourf ( x1, x2, y_pred, alpha = 0.3, cmap = CUSTOM_CMAP ) plot_decision_boundary ( tree_clf, X, Y ) plt. ![]() plot ( x, # petal length on X axis (the ones that equal to target) x, # petal width on Y axis (the ones that equal to target) color_map, label = target_name ) x1s = np. Import matplotlib.pyplot as plt from lors import ListedColormap CUSTOM_CMAP = ListedColormap () # helper function to plot the boundaries def plot_decision_boundary ( clf, x, y ): color_map = for target_index, target_name in enumerate ( iris. Requirement already satisfied: setuptools in c:\local\anaconda3\envs\mltech\lib\site-packages (from kiwisolver>=1.0.1->matplotlib->dtreeviz) (41.6.0.post20191030) Requirement already satisfied: six>=1.5 in c:\local\anaconda3\envs\mltech\lib\site-packages (from python-dateutil>=2.6.1->pandas->dtreeviz) (1.12.0) Requirement already satisfied: scipy>=0.17.0 in c:\local\anaconda3\envs\mltech\lib\site-packages (from scikit-learn->dtreeviz) (1.3.1) Requirement already satisfied: joblib>=0.11 in c:\local\anaconda3\envs\mltech\lib\site-packages (from scikit-learn->dtreeviz) (0.13.2) Requirement already satisfied: kiwisolver>=1.0.1 in c:\local\anaconda3\envs\mltech\lib\site-packages (from matplotlib->dtreeviz) (1.1.0) Requirement already satisfied: cycler>=0.10 in c:\local\anaconda3\envs\mltech\lib\site-packages (from matplotlib->dtreeviz) (0.10.0) Requirement already satisfied: python-dateutil>=2.6.1 in c:\local\anaconda3\envs\mltech\lib\site-packages (from pandas->dtreeviz) (2.8.0) Requirement already satisfied: pytz>=2017.2 in c:\local\anaconda3\envs\mltech\lib\site-packages (from pandas->dtreeviz) (2019.3) Requirement already satisfied: graphviz>=0.9 in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (0.13) ![]() Requirement already satisfied: colour in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (0.1.5) Requirement already satisfied: scikit-learn in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (0.21.3) Requirement already satisfied: numpy in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (1.17.2) Requirement already satisfied: matplotlib in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (3.1.1) Requirement already satisfied: pandas in c:\local\anaconda3\envs\mltech\lib\site-packages (from dtreeviz) (0.25.2) Requirement already satisfied: dtreeviz in c:\local\anaconda3\envs\mltech\lib\site-packages (0.6) If the node is completely pure (all instances belong to the same class), the gini impurity becomes zero.
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