マシン学習/意思決定ツリー

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

iris = load_iris() 
X = iris.data[:,2:] # petal length, width
y = iris.target

tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)
tree_clf.fit(X, y)

DecisionTreeClassifier(max_depth=2, random_state=42)

from sklearn.tree import export_graphviz

export_graphviz(tree_clf, 
                out_file="iris_tree.dot", 
                class_names=iris.target_names,
                feature_names = iris.feature_names[2:],
                rounded = True,
                filled = True
               )

import graphviz
with open("iris_tree.dot") as f:
    dot_graph = f.read()
graphviz.Source(dot_graph)

tree_clf.predict_proba([[5, 1.5]])

array([[0.        , 0.90740741, 0.09259259]])

tree_clf.predict([[5, 1.5]])

array([1])

tree_clf.feature_importances_

array([0.56199095, 0.43800905])

iris.feature_names[2:]

['petal length (cm)', 'petal width (cm)']

np.random.seed(42)

m = 200
X = np.random.rand(m, 1)
y = 4 * (X-0.5) ** 2
y = y  + np.random.rand(m,1) / 10

from sklearn.tree import DecisionTreeRegressor

tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)
tree_reg.fit(X, y)

DecisionTreeRegressor(max_depth=2, random_state=42)

export_graphviz(
    tree_reg,
    out_file="regression_tree.dot",
    feature_names = ["x1"], 
    rounded=True, 
    filled=True)

import graphviz

with open("regression_tree.dot") as f:
    dot_graph = f.read()
    
graphviz.Source(dot_graph)    

tree_reg.predict([[0.6]])

array([0.22015219])