DecisionTreeClassifier

from sklearn.datasets import load_wine # 红酒数据集

from sklearn.tree import DecisionTreeClassifier, export_graphviz # 决策树， 画树

from sklearn.model_selection import train_test_split # 数据集划分

import graphviz

import matplotlib.pyplot as plt


# 实例化红酒数据集

wine = load_wine()


# 划分测试集和训练集

x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.25)


# 实例化决策树

clf = DecisionTreeClassifier(

                            criterion="entropy"

                            ,random_state=30

                            ,splitter="random"

                            ,max_depth=4

)

clf.fit(x_train, y_train)

score = clf.score(x_test, y_test)

score


0.9333333333333333


# 查看每个特征的重要性

feature_names = ['酒精', '苹果酸', '灰', '灰的碱性', '镁', '总酚', '类黄酮', '非黄烷类酚类', '花青素', '颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']

[*zip(feature_names, clf.feature_importances_)]


[('酒精', 0.2251130582973216),

 ('苹果酸', 0.0),

 ('灰', 0.02596756412075755),

 ('灰的碱性', 0.0),

 ('镁', 0.0),

 ('总酚', 0.0),

 ('类黄酮', 0.43464628982715003),

 ('非黄烷类酚类', 0.03292950151904385),

 ('花青素', 0.02494017691000391),

 ('颜色强度', 0.0),

 ('色调', 0.03635605431269296),

 ('od280/od315稀释葡萄酒', 0.17795967993642653),

 ('脯氨酸', 0.04208767507660348)]


# 画出这棵树

data_dot = export_graphviz(

                            clf

                            ,feature_names=feature_names

                            ,class_names=["红酒","黄酒","啤酒"]

                            ,filled=True

                            ,rounded=True

)

grap = graphviz.Source(data_dot)

grap

​

​

# 展示max_depth各值对准确率影响的曲线

test = []

for i in range(10):

    clf = DecisionTreeClassifier(

        criterion="entropy", random_state=30, splitter="random", max_depth=i+1

    )

    clf = clf.fit(x_train, y_train)

    score = clf.score(x_test, y_test)

    test.append(score)

plt.plot(range(1, 11),test, color="red", label="max_depth")

plt.legend()

plt.show()

​

​

DecisionTreeClassRegression

import pandas as pd # 数据处理

from sklearn.tree import DecisionTreeRegressor # 回归树

from sklearn.model_selection import cross_val_score     # 交叉验证


# 导入数据

df = pd.read_csv("./data//boston_house_prices.csv")

df.head()
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CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT MEDV
0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2 
# 特征值

data = df.iloc[:,:-1]

data
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CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
... ... ... ... ... ... ... ... ... ... ... ... ... ...
501 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 21.0 391.99 9.67
502 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 21.0 396.90 9.08
503 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 21.0 396.90 5.64
504 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 21.0 393.45 6.48
505 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 21.0 396.90 7.88

506 rows × 13 columns

# 目标值

target = df.iloc[:,-1:]

target
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MEDV
0 24.0
1 21.6
2 34.7
3 33.4
4 36.2
... ...
501 22.4
502 20.6
503 23.9
504 22.0
505 11.9

506 rows × 1 columns

# 实例化回归树

clr = DecisionTreeRegressor(random_state=0)


# 实例化交叉验证

cross = cross_val_score(clr, data, target, scoring="neg_mean_squared_error", cv=10)

cross


array([-18.08941176, -10.61843137, -16.31843137, -44.97803922,

       -17.12509804, -49.71509804, -12.9986    , -88.4514    ,

       -55.7914    , -25.0816    ])

一维回归图像绘制

import numpy as np

from sklearn.tree import DecisionTreeRegressor

import matplotlib.pyplot as plt


rng = np.random.RandomState(1)

rng


RandomState(MT19937) at 0x7FC5EEAAAF40


x = np.sort(5 * rng.rand(80,1), axis=0)

x


array([[5.71874087e-04],

       [9.14413867e-02],

       [9.68347894e-02],

       [1.36937966e-01],

       [1.95273916e-01],

       [2.49767295e-01],

       [2.66812726e-01],

       [4.25221057e-01],

       [4.61692974e-01],

       [4.91734169e-01],

       [5.11672144e-01],

       [5.16130033e-01],

       [6.50142861e-01],

       [6.87373521e-01],

       [6.96381736e-01],

       [7.01934693e-01],

       [7.33642875e-01],

       [7.33779454e-01],

       [8.26770986e-01],

       [8.49152098e-01],

       [9.31301057e-01],

       [9.90507445e-01],

       [1.02226125e+00],

       [1.05814058e+00],

       [1.32773330e+00],

       [1.40221996e+00],

       [1.43887669e+00],

       [1.46807074e+00],

       [1.51166286e+00],

       [1.56712089e+00],

       [1.57757816e+00],

       [1.72780364e+00],

       [1.73882930e+00],

       [1.98383737e+00],

       [1.98838418e+00],

       [2.07027994e+00],

       [2.07089635e+00],

       [2.08511002e+00],

       [2.08652401e+00],

       [2.09597257e+00],

       [2.10553813e+00],

       [2.23946763e+00],

       [2.45786580e+00],

       [2.57444556e+00],

       [2.66582642e+00],

       [2.67948203e+00],

       [2.69408367e+00],

       [2.79344914e+00],

       [2.87058803e+00],

       [2.93277520e+00],

       [2.94652768e+00],

       [3.31897323e+00],

       [3.35233755e+00],

       [3.39417766e+00],

       [3.42609750e+00],

       [3.43250464e+00],

       [3.45938557e+00],

       [3.46161308e+00],

       [3.47200079e+00],

       [3.49879180e+00],

       [3.60162247e+00],

       [3.62998993e+00],

       [3.74082827e+00],

       [3.75072157e+00],

       [3.75406052e+00],

       [3.94639664e+00],

       [4.00372284e+00],

       [4.03695644e+00],

       [4.17312836e+00],

       [4.38194576e+00],

       [4.39058718e+00],

       [4.39071252e+00],

       [4.47303332e+00],

       [4.51700958e+00],

       [4.54297752e+00],

       [4.63754290e+00],

       [4.72297378e+00],

       [4.78944765e+00],

       [4.84130788e+00],

       [4.94430544e+00]])


y = np.sin(x).ravel()


y[::5] += 3 * (0.5 - rng.rand(16))

y


array([-1.1493464 ,  0.09131401,  0.09668352,  0.13651039,  0.19403525,

       -0.12383814,  0.26365828,  0.41252216,  0.44546446,  0.47215529,

       -0.26319138,  0.49351799,  0.60530013,  0.63450933,  0.64144608,

        1.09900119,  0.66957978,  0.66968122,  0.73574834,  0.75072053,

        1.4926134 ,  0.8363043 ,  0.8532893 ,  0.87144496,  0.97060533,

       -0.20183403,  0.99131122,  0.99472837,  0.99825213,  0.99999325,

        1.21570343,  0.98769965,  0.98591565,  0.9159044 ,  0.91406986,

       -0.51669013,  0.8775346 ,  0.87063055,  0.86993408,  0.86523559,

        0.37007575,  0.78464608,  0.63168655,  0.53722799,  0.45801971,

        0.08075119,  0.43272116,  0.34115328,  0.26769953,  0.20730318,

        1.34959235, -0.17645185, -0.20918837, -0.24990778, -0.28068224,

       -1.63529379, -0.31247075, -0.31458595, -0.32442911, -0.34965155,

       -0.29371122, -0.46921115, -0.56401144, -0.57215326, -0.57488849,

       -0.95586361, -0.75923066, -0.78043659, -0.85808859, -0.94589863,

       -0.6730775 , -0.94870673, -0.97149093, -0.98097408, -0.98568417,

       -0.20828128, -0.99994398, -0.99703245, -0.99170146, -0.9732277 ])


reg1 = DecisionTreeRegressor(max_depth=2)

reg2 = DecisionTreeRegressor(max_depth=5)

reg1.fit(x, y)

reg2.fit(x, y)


DecisionTreeRegressor(max_depth=5)


x_test = np.arange(0.0, 5.0, 0.01)[:,np.newaxis]

x_test


array([[0.  ],

       [0.01],

       [0.02],

       [0.03],

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       [4.93],

       [4.94],

       [4.95],

       [4.96],

       [4.97],

       [4.98],

       [4.99]])


y1 = reg1.predict(x_test)

y2 = reg2.predict(x_test)


plt.figure()

plt.scatter(x,y,s=20, edgecolors="black", c="darkorange", label="data")

plt.plot(x_test, y1, color="cornflowerblue",label="max_depth=2",linewidth=2)

plt.plot(x_test, y2, color="yellowgreen",label="max_depth=5",linewidth=2)

plt.xlabel("data")

plt.ylabel("target")

plt.title("Decision Tree Regressor")

plt.legend()

plt.show()

​

​

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