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()

# 特征值
data = df.iloc[:,:-1]
data

# 目标值
target = df.iloc[:,-1:]
target

# 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

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       [0.01],
       [0.02],
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       [0.1 ],
       [0.11],
       [0.12],
       [0.13],
       [0.14],
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       [0.16],
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       [0.2 ],
       [0.21],
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       [0.3 ],
       [0.31],
       [0.32],
       [0.33],
       [0.34],
       [0.35],
       [0.36],
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       [0.39],
       [0.4 ],
       [0.41],
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       [1.89],
       [1.9 ],
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       [1.94],
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       [1.96],
       [1.97],
       [1.98],
       [1.99],
       [2.  ],
       [2.01],
       [2.02],
       [2.03],
       [2.04],
       [2.05],
       [2.06],
       [2.07],
       [2.08],
       [2.09],
       [2.1 ],
       [2.11],
       [2.12],
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       [2.15],
       [2.16],
       [2.17],
       [2.18],
       [2.19],
       [2.2 ],
       [2.21],
       [2.22],
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       [2.24],
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       [2.26],
       [2.27],
       [2.28],
       [2.29],
       [2.3 ],
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       [2.32],
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       [2.4 ],
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       [2.59],
       [2.6 ],
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       [2.63],
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       [2.67],
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       [2.69],
       [2.7 ],
       [2.71],
       [2.72],
       [2.73],
       [2.74],
       [2.75],
       [2.76],
       [2.77],
       [2.78],
       [2.79],
       [2.8 ],
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       [2.82],
       [2.83],
       [2.84],
       [2.85],
       [2.86],
       [2.87],
       [2.88],
       [2.89],
       [2.9 ],
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       [2.92],
       [2.93],
       [2.94],
       [2.95],
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       [3.  ],
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       [3.02],
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       [3.07],
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       [4.9 ],
       [4.91],
       [4.92],
       [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()

​

​