Tree 0 petal length (cm) ≤ 2.45 gini = 0.667 samples = 150 value = class = setosa 1 gini = 0.0 samples = 50 value = class = setosa 0->1 True 2 petal width (cm) ≤ 1.75 gini = 0.5 samples = 100 value = class = versicolor 0->2 False 3 petal length (cm) ≤ 4.95 gini = 0.168 samples = 54 value = class = versicolor 2->3 12 petal length (cm) ≤ 4.85 gini = 0.043 samples = 46 value = class = virginica 2->12 4 petal width (cm) ≤ 1.65 gini = 0.041 samples = 48 value = class = versicolor 3->4 7 petal width (cm) ≤ 1.55 gini = 0.444 samples = 6 value = class = virginica 3->7 5 gini = 0.0 samples = 47 value = class = versicolor 4->5 6 gini = 0.0 samples = 1 value = class = virginica 4->6 8 gini = 0.0 samples = 3 value = class = virginica 7->8 9 petal length (cm) ≤ 5.45 gini = 0.444 samples = 3 value = class = versicolor 7->9 10 gini = 0.0 samples = 2 value = class = versicolor 9->10 11 gini = 0.0 samples = 1 value = class = virginica 9->11 13 sepal width (cm) ≤ 3.1 gini = 0.444 samples = 3 value = class = virginica 12->13 16 gini = 0.0 samples = 43 value = class = virginica 12->16 14 gini = 0.0 samples = 2 value = class = virginica 13->14 15 gini = 0. Visualizing the decision trees can be really simple using a combination of scikit-learn and matplotlib. The visualization decision tree is a tremendous task to learn, understand interpretation and working of the models.

Visualize decision tree