DecisionTreeClassifier&DecisionTreeClassRegression

(资料图片仅供参考)

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 graphvizimport 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

506 rows × 13 columns

# 目标值target = df.iloc[:,-1:]target

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 npfrom sklearn.tree import DecisionTreeRegressorimport 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],       [0.04],       [0.05],       [0.06],       [0.07],       [0.08],       [0.09],       [0.1 ],       [0.11],       [0.12],       [0.13],       [0.14],       [0.15],       [0.16],       [0.17],       [0.18],       [0.19],       [0.2 ],       [0.21],       [0.22],       [0.23],       [0.24],       [0.25],       [0.26],       [0.27],       [0.28],       [0.29],       [0.3 ],       [0.31],       [0.32],       [0.33],       [0.34],       [0.35],       [0.36],       [0.37],       [0.38],       [0.39],       [0.4 ],       [0.41],       [0.42],       [0.43],       [0.44],       [0.45],       [0.46],       [0.47],       [0.48],       [0.49],       [0.5 ],       [0.51],       [0.52],       [0.53],       [0.54],       [0.55],       [0.56],       [0.57],       [0.58],       [0.59],       [0.6 ],       [0.61],       [0.62],       [0.63],       [0.64],       [0.65],       [0.66],       [0.67],       [0.68],       [0.69],       [0.7 ],       [0.71],       [0.72],       [0.73],       [0.74],       [0.75],       [0.76],       [0.77],       [0.78],       [0.79],       [0.8 ],       [0.81],       [0.82],       [0.83],       [0.84],       [0.85],       [0.86],       [0.87],       [0.88],       [0.89],       [0.9 ],       [0.91],       [0.92],       [0.93],       [0.94],       [0.95],       [0.96],       [0.97],       [0.98],       [0.99],       [1.  ],       [1.01],       [1.02],       [1.03],       [1.04],       [1.05],       [1.06],       [1.07],       [1.08],       [1.09],       [1.1 ],       [1.11],       [1.12],       [1.13],       [1.14],       [1.15],       [1.16],       [1.17],       [1.18],       [1.19],       [1.2 ],       [1.21],       [1.22],       [1.23],       [1.24],       [1.25],       [1.26],       [1.27],       [1.28],       [1.29],       [1.3 ],       [1.31],       [1.32],       [1.33],       [1.34],       [1.35],       [1.36],       [1.37],       [1.38],       [1.39],       [1.4 ],       [1.41],       [1.42],       [1.43],       [1.44],       [1.45],       [1.46],       [1.47],       [1.48],       [1.49],       [1.5 ],       [1.51],       [1.52],       [1.53],       [1.54],       [1.55],       [1.56],       [1.57],       [1.58],       [1.59],       [1.6 ],       [1.61],       [1.62],       [1.63],       [1.64],       [1.65],       [1.66],       [1.67],       [1.68],       [1.69],       [1.7 ],       [1.71],       [1.72],       [1.73],       [1.74],       [1.75],       [1.76],       [1.77],       [1.78],       [1.79],       [1.8 ],       [1.81],       [1.82],       [1.83],       [1.84],       [1.85],       [1.86],       [1.87],       [1.88],       [1.89],       [1.9 ],       [1.91],       [1.92],       [1.93],       [1.94],       [1.95],       [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],       [2.13],       [2.14],       [2.15],       [2.16],       [2.17],       [2.18],       [2.19],       [2.2 ],       [2.21],       [2.22],       [2.23],       [2.24],       [2.25],       [2.26],       [2.27],       [2.28],       [2.29],       [2.3 ],       [2.31],       [2.32],       [2.33],       [2.34],       [2.35],       [2.36],       [2.37],       [2.38],       [2.39],       [2.4 ],       [2.41],       [2.42],       [2.43],       [2.44],       [2.45],       [2.46],       [2.47],       [2.48],       [2.49],       [2.5 ],       [2.51],       [2.52],       [2.53],       [2.54],       [2.55],       [2.56],       [2.57],       [2.58],       [2.59],       [2.6 ],       [2.61],       [2.62],       [2.63],       [2.64],       [2.65],       [2.66],       [2.67],       [2.68],       [2.69],       [2.7 ],       [2.71],       [2.72],       [2.73],       [2.74],       [2.75],       [2.76],       [2.77],       [2.78],       [2.79],       [2.8 ],       [2.81],       [2.82],       [2.83],       [2.84],       [2.85],       [2.86],       [2.87],       [2.88],       [2.89],       [2.9 ],       [2.91],       [2.92],       [2.93],       [2.94],       [2.95],       [2.96],       [2.97],       [2.98],       [2.99],       [3.  ],       [3.01],       [3.02],       [3.03],       [3.04],       [3.05],       [3.06],       [3.07],       [3.08],       [3.09],       [3.1 ],       [3.11],       [3.12],       [3.13],       [3.14],       [3.15],       [3.16],       [3.17],       [3.18],       [3.19],       [3.2 ],       [3.21],       [3.22],       [3.23],       [3.24],       [3.25],       [3.26],       [3.27],       [3.28],       [3.29],       [3.3 ],       [3.31],       [3.32],       [3.33],       [3.34],       [3.35],       [3.36],       [3.37],       [3.38],       [3.39],       [3.4 ],       [3.41],       [3.42],       [3.43],       [3.44],       [3.45],       [3.46],       [3.47],       [3.48],       [3.49],       [3.5 ],       [3.51],       [3.52],       [3.53],       [3.54],       [3.55],       [3.56],       [3.57],       [3.58],       [3.59],       [3.6 ],       [3.61],       [3.62],       [3.63],       [3.64],       [3.65],       [3.66],       [3.67],       [3.68],       [3.69],       [3.7 ],       [3.71],       [3.72],       [3.73],       [3.74],       [3.75],       [3.76],       [3.77],       [3.78],       [3.79],       [3.8 ],       [3.81],       [3.82],       [3.83],       [3.84],       [3.85],       [3.86],       [3.87],       [3.88],       [3.89],       [3.9 ],       [3.91],       [3.92],       [3.93],       [3.94],       [3.95],       [3.96],       [3.97],       [3.98],       [3.99],       [4.  ],       [4.01],       [4.02],       [4.03],       [4.04],       [4.05],       [4.06],       [4.07],       [4.08],       [4.09],       [4.1 ],       [4.11],       [4.12],       [4.13],       [4.14],       [4.15],       [4.16],       [4.17],       [4.18],       [4.19],       [4.2 ],       [4.21],       [4.22],       [4.23],       [4.24],       [4.25],       [4.26],       [4.27],       [4.28],       [4.29],       [4.3 ],       [4.31],       [4.32],       [4.33],       [4.34],       [4.35],       [4.36],       [4.37],       [4.38],       [4.39],       [4.4 ],       [4.41],       [4.42],       [4.43],       [4.44],       [4.45],       [4.46],       [4.47],       [4.48],       [4.49],       [4.5 ],       [4.51],       [4.52],       [4.53],       [4.54],       [4.55],       [4.56],       [4.57],       [4.58],       [4.59],       [4.6 ],       [4.61],       [4.62],       [4.63],       [4.64],       [4.65],       [4.66],       [4.67],       [4.68],       [4.69],       [4.7 ],       [4.71],       [4.72],       [4.73],       [4.74],       [4.75],       [4.76],       [4.77],       [4.78],       [4.79],       [4.8 ],       [4.81],       [4.82],       [4.83],       [4.84],       [4.85],       [4.86],       [4.87],       [4.88],       [4.89],       [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()

​

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