(相关资料图)
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)score0.9333333333333333# 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()
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()| 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| 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| 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)crossarray([-18.08941176, -10.61843137, -16.31843137, -44.97803922, -17.12509804, -49.71509804, -12.9986 , -88.4514 , -55.7914 , -25.0816 ])import numpy as npfrom sklearn.tree import DecisionTreeRegressorimport matplotlib.pyplot as pltrng = np.random.RandomState(1)rngRandomState(MT19937) at 0x7FC5EEAAAF40x = np.sort(5 * rng.rand(80,1), axis=0)xarray([[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))yarray([-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_testarray([[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()
