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【XGBoost 多分类】XGBoost解决多分类问题_xgboost 多分类
作者：很楠不爱3 | 2024-02-16 22:42:22
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xgboost 多分类
下面将以一个例子来讲解 XGBoost 解决多分类问题。
1、下载数据集，数据集我们采用小麦种子数据集，该数据集有3类，已知小麦种子包含 7个特征，分别为面积，周长，紧凑度，仔粒长度，仔粒宽度，不对称系数，仔粒腹沟长度，小麦类别为1,2，3
linux --下载数据集：
wget https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt
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window–下载数据集：
将地址复制到浏览器即可下载
https://archive.ics.uci.edu/ml/machine-learning-databases/00236/seeds_dataset.txt
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文件名叫 seeds_dataset.txt 数据类似如下：
15.26 14.84 0.871 5.763 3.312 2.221 5.22 1
14.88 14.57 0.8811 5.554 3.333 1.018 4.956 1
14.29 14.09 0.905 5.291 3.337 2.699 4.825 1
13.84 13.94 0.8955 5.324 3.379 2.259 4.805 1
16.14 14.99 0.9034 5.658 3.562 1.355 5.175 1
14.38 14.21 0.8951 5.386 3.312 2.462 4.956 1
14.69 14.49 0.8799 5.563 3.259 3.586 5.219 1
14.11 14.1 0.8911 5.42 3.302 2.7 5 1
16.63 15.46 0.8747 6.053 3.465 2.04 5.877 1
16.44 15.25 0.888 5.884 3.505 1.969 5.533 1
15.26 14.85 0.8696 5.714 3.242 4.543 5.314 1
14.03 14.16 0.8796 5.438 3.201 1.717 5.001 1
13.89 14.02 0.888 5.439 3.199 3.986 4.738 1
.....
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# -*- coding: utf-8 -*- import pandas as pd import xgboost as xgb import numpy as np import warnings warnings.filterwarnings('ignore') from sklearn.model_selection import train_test_split data_path='./datasets/seeds_dataset.txt' data=pd.read_csv(data_path,header=None,sep='\s+',converters={7:lambda x:int(x)-1}) data.rename(columns={7:'lable'},inplace=True) print(data) # # # 生产一个随机数并选择小于0.8的数据 # mask=np.random.rand(len(data))<0.8 # train=data[mask] # test=data[~mask] # # # 生产DMatrix # xgb_train=xgb.DMatrix(train.iloc[:,:6],label=train.lable) # xgb_test=xgb.DMatrix(test.iloc[:,:6],label=test.lable) X=data.iloc[:,:6] Y=data.iloc[:,7] X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=100) xgb_train=xgb.DMatrix(X_train,label=y_train) xgb_test=xgb.DMatrix(X_test,label=y_test) # 设置模型参数 params={ 'objective':'multi:softmax', 'eta':0.1, 'max_depth':5, 'num_class':3 } watchlist=[(xgb_train,'train'),(xgb_test,'test')] # 设置训练轮次，这里设置60轮 num_round=60 bst=xgb.train(params,xgb_train,num_round,watchlist) # 模型预测 pred=bst.predict(xgb_test) print(pred) #模型评估 # error_rate=np.sum(pred!=test.lable)/test.lable.shape[0] error_rate=np.sum(pred!=y_test)/y_test.shape[0] print('测试集错误率(softmax):{}'.format(error_rate)) accuray=1-error_rate print('测试集准确率：%.4f' %accuray) # 模型保存 bst.save_model("./datasets/002.model") # 模型加载 bst=xgb.Booster() bst.load_model("./datasets/002.model") pred=bst.predict(xgb_test) print(pred)
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运行结果：
0 1 2 3 4 5 6 lable 0 15.26 14.84 0.8710 5.763 3.312 2.221 5.220 0 1 14.88 14.57 0.8811 5.554 3.333 1.018 4.956 0 2 14.29 14.09 0.9050 5.291 3.337 2.699 4.825 0 3 13.84 13.94 0.8955 5.324 3.379 2.259 4.805 0 4 16.14 14.99 0.9034 5.658 3.562 1.355 5.175 0 .. ... ... ... ... ... ... ... ... 205 12.19 13.20 0.8783 5.137 2.981 3.631 4.870 2 206 11.23 12.88 0.8511 5.140 2.795 4.325 5.003 2 207 13.20 13.66 0.8883 5.236 3.232 8.315 5.056 2 208 11.84 13.21 0.8521 5.175 2.836 3.598 5.044 2 209 12.30 13.34 0.8684 5.243 2.974 5.637 5.063 2 [210 rows x 8 columns] [0] train-merror:0.012739 test-merror:0.075472 [1] train-merror:0.012739 test-merror:0.056604 [2] train-merror:0.006369 test-merror:0.075472 [3] train-merror:0.012739 test-merror:0.075472 [4] train-merror:0.006369 test-merror:0.075472 [5] train-merror:0 test-merror:0.075472 [6] train-merror:0 test-merror:0.075472 [7] train-merror:0 test-merror:0.075472 [8] train-merror:0 test-merror:0.075472 [9] train-merror:0 test-merror:0.075472 [10] train-merror:0 test-merror:0.075472 [11] train-merror:0 test-merror:0.075472 [12] train-merror:0 test-merror:0.075472 [13] train-merror:0 test-merror:0.075472 [14] train-merror:0 test-merror:0.075472 [15] train-merror:0 test-merror:0.075472 [16] train-merror:0 test-merror:0.075472 [17] train-merror:0 test-merror:0.075472 [18] train-merror:0 test-merror:0.075472 [19] train-merror:0 test-merror:0.075472 [20] train-merror:0 test-merror:0.075472 [21] train-merror:0 test-merror:0.075472 [22] train-merror:0 test-merror:0.075472 [23] train-merror:0 test-merror:0.075472 [24] train-merror:0 test-merror:0.075472 [25] train-merror:0 test-merror:0.075472 [26] train-merror:0 test-merror:0.075472 [27] train-merror:0 test-merror:0.075472 [28] train-merror:0 test-merror:0.075472 [29] train-merror:0 test-merror:0.075472 [30] train-merror:0 test-merror:0.075472 [31] train-merror:0 test-merror:0.075472 [32] train-merror:0 test-merror:0.075472 [33] train-merror:0 test-merror:0.075472 [34] train-merror:0 test-merror:0.075472 [35] train-merror:0 test-merror:0.075472 [36] train-merror:0 test-merror:0.075472 [37] train-merror:0 test-merror:0.075472 [38] train-merror:0 test-merror:0.075472 [39] train-merror:0 test-merror:0.075472 [40] train-merror:0 test-merror:0.075472 [41] train-merror:0 test-merror:0.075472 [42] train-merror:0 test-merror:0.075472 [43] train-merror:0 test-merror:0.075472 [44] train-merror:0 test-merror:0.075472 [45] train-merror:0 test-merror:0.075472 [46] train-merror:0 test-merror:0.075472 [47] train-merror:0 test-merror:0.075472 [48] train-merror:0 test-merror:0.075472 [49] train-merror:0 test-merror:0.075472 [50] train-merror:0 test-merror:0.075472 [51] train-merror:0 test-merror:0.075472 [52] train-merror:0 test-merror:0.075472 [53] train-merror:0 test-merror:0.075472 [54] train-merror:0 test-merror:0.075472 [55] train-merror:0 test-merror:0.075472 [56] train-merror:0 test-merror:0.075472 [57] train-merror:0 test-merror:0.075472 [58] train-merror:0 test-merror:0.075472 [59] train-merror:0 test-merror:0.075472 [0. 2. 2. 2. 1. 0. 2. 1. 2. 2. 1. 1. 1. 0. 1. 0. 2. 1. 0. 2. 1. 0. 1. 0. 2. 2. 1. 2. 0. 0. 2. 0. 2. 2. 1. 2. 2. 2. 2. 1. 1. 0. 1. 1. 0. 2. 0. 2. 2. 1. 0. 2. 2.] 测试集错误率(softmax):0.07547169811320754 测试集准确率：0.9245 [0. 2. 2. 2. 1. 0. 2. 1. 2. 2. 1. 1. 1. 0. 1. 0. 2. 1. 0. 2. 1. 0. 1. 0. 2. 2. 1. 2. 0. 0. 2. 0. 2. 2. 1. 2. 2. 2. 2. 1. 1. 0. 1. 1. 0. 2. 0. 2. 2. 1. 0. 2. 2.] Process finished with exit code 0
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