基于随机森林模型的红酒品质分析 |
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基于随机森林模型的红酒品质分析
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文章目录
一、数据获取1.1数据集基本信息1.2数据具体情况1.3导入数据集
二、预处理和探索2.1查看数据基本情况2.2处理数据集2.3探索特征属性和目标属性的相关性2.4清洗数据2.5选取训练和测试数据2.6标准化处理
三、机器学习建模3.1机器学习模型选择3.2训练模型3.3预测结果判断
四、调参五、整体代码六、参考材料
一、数据获取
数据集：Wine Quality Data Set UCI葡萄酒数据集https://archive.ics.uci.edu/ml/datasets/wine+quality 通过网站上数据集的摘要了解数据集的基本情况吗，发现UCI葡萄酒数据集包括两份：葡萄牙北部的红色和白色葡萄酒样本 该样本常用于数据分析和机器学习分类等任务 选择红葡萄酒数据集进行分析 1.1数据集基本信息 Attribute Information: #数据集中各属性的说明 For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): #输入变量（特征属性），基于物理化学测试 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar #残糖 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH #pH值 10 - sulphates 11 - alcohol #酒精度 Output variable (based on sensory data): #输出变量（目标属性） 12 - quality (score between 0 and 10) #葡萄酒的质量评分 1.2数据具体情况
数据有表头，数据间用;隔开 利用pandas完成数据的读取和预处理 利用pandas模块中的read_csv()函数，并将其参数sep的值设为’;'就可以读取数据 import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV import warnings warnings.filterwarnings('ignore') try: #读取数据 wine = pd.read_csv('winequality-red.csv', sep = ';') #将数据存在wine中，wine为DataFrame对象 except: print("Cannot find the file!") 二、预处理和探索 2.1查看数据基本情况 wine.info() #查看数据基本情况 RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns): fixed acidity 1599 non-null float64 volatile acidity 1599 non-null float64 citric acid 1599 non-null float64 residual sugar 1599 non-null float64 chlorides 1599 non-null float64 free sulfur dioxide 1599 non-null float64 total sulfur dioxide 1599 non-null float64 density 1599 non-null float64 pH 1599 non-null float64 sulphates 1599 non-null float64 alcohol 1599 non-null float64 quality 1599 non-null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB 2.2处理数据集 检查重复记录duplicated()检查Series或DataFrame对象是否有重复记录——有True，无False,返回结果用sum()方法计算总和就能获得重复的行数 wine.duplicated().sum() #检查DataFrame是否有重复记录，并用sum()计算重复行数 Out[3]: 240 wine=wine.drop_duplicates() #若有重复记录删除并重新赋值给wine对象 wine Out[5]: fixed acidity volatile acidity ... alcohol quality 0 7.4 0.700 ... 9.4 5 1 7.8 0.880 ... 9.8 5 2 7.8 0.760 ... 9.8 5 3 11.2 0.280 ... 9.8 6 5 7.4 0.660 ... 9.4 5 6 7.9 0.600 ... 9.4 5 7 7.3 0.650 ... 10.0 7 8 7.8 0.580 ... 9.5 7 9 7.5 0.500 ... 10.5 5 10 6.7 0.580 ... 9.2 5 12 5.6 0.615 ... 9.9 5 13 7.8 0.610 ... 9.1 5 14 8.9 0.620 ... 9.2 5 15 8.9 0.620 ... 9.2 5 16 8.5 0.280 ... 10.5 7 17 8.1 0.560 ... 9.3 5 18 7.4 0.590 ... 9.0 4 19 7.9 0.320 ... 9.2 6 20 8.9 0.220 ... 9.4 6 21 7.6 0.390 ... 9.7 5 22 7.9 0.430 ... 9.5 5 23 8.5 0.490 ... 9.4 5 24 6.9 0.400 ... 9.7 6 25 6.3 0.390 ... 9.3 5 26 7.6 0.410 ... 9.5 5 28 7.1 0.710 ... 9.4 5 29 7.8 0.645 ... 9.8 6 30 6.7 0.675 ... 10.1 5 31 6.9 0.685 ... 10.6 6 32 8.3 0.655 ... 9.8 5 ... ... ... ... ... 1566 6.7 0.160 ... 11.2 6 1568 7.0 0.560 ... 9.2 5 1569 6.2 0.510 ... 11.5 6 1570 6.4 0.360 ... 12.4 6 1571 6.4 0.380 ... 11.1 6 1572 7.3 0.690 ... 9.5 5 1573 6.0 0.580 ... 12.5 6 1574 5.6 0.310 ... 10.5 6 1575 7.5 0.520 ... 11.8 6 1576 8.0 0.300 ... 10.8 6 1577 6.2 0.700 ... 11.9 6 1578 6.8 0.670 ... 11.3 6 1579 6.2 0.560 ... 11.3 5 1580 7.4 0.350 ... 11.9 6 1582 6.1 0.715 ... 11.9 5 1583 6.2 0.460 ... 9.8 5 1584 6.7 0.320 ... 11.6 7 1585 7.2 0.390 ... 11.5 6 1586 7.5 0.310 ... 11.4 6 1587 5.8 0.610 ... 10.9 6 1588 7.2 0.660 ... 12.8 6 1589 6.6 0.725 ... 9.2 5 1590 6.3 0.550 ... 11.6 6 1591 5.4 0.740 ... 11.6 6 1592 6.3 0.510 ... 11.0 6 1593 6.8 0.620 ... 9.5 6 1594 6.2 0.600 ... 10.5 5 1595 5.9 0.550 ... 11.2 6 1597 5.9 0.645 ... 10.2 5 1598 6.0 0.310 ... 11.0 6 [1359 rows x 12 columns] 简单查看目标属性quality,并查看quality属性每一类的分布情况，发现符合正态分布 wine.describe() #查看数据基本信息 Out[6]: fixed acidity volatile acidity ... alcohol quality count 1359.000000 1359.000000 ... 1359.000000 1359.000000 mean 8.310596 0.529478 ... 10.432315 5.623252 std 1.736990 0.183031 ... 1.082065 0.823578 min 4.600000 0.120000 ... 8.400000 3.000000 25% 7.100000 0.390000 ... 9.500000 5.000000 50% 7.900000 0.520000 ... 10.200000 6.000000 75% 9.200000 0.640000 ... 11.100000 6.000000 max 15.900000 1.580000 ... 14.900000 8.000000 [8 rows x 12 columns] wine.quality.value_counts() #查看quality属性具体每一类有多少个值 Out[8]: 5 577 6 535 7 167 4 53 8 17 3 10 Name: quality, dtype: int64 2.3探索特征属性和目标属性的相关性 通过相关性发现与volatile acidity和alcohol相关性比较大，前者为负相关，后者为正相关 再通过绘图查看每个quality值对应的volatile acidity和alcohol属性的均值的分布情况,直观的查看他们之间的相关性——使用seaborn模块的barplot()函数来处理 wine.corr().quality #wine与属性之间的相关性 Out[11]: fixed acidity 0.119024 volatile acidity -0.395214 citric acid 0.228057 residual sugar 0.013640 chlorides -0.130988 free sulfur dioxide -0.050463 total sulfur dioxide -0.177855 density -0.184252 pH -0.055245 sulphates 0.248835 alcohol 0.480343 quality 1.000000 Name: quality, dtype: float64 sns.barplot(x='quality',y='volatile acidity',data=wine) #通过绘图查看均值的分布情况，了解相关性[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-deS3JuHd-1680958755074)(D:\typora\photo\volatile acidity.png)] 看到quality为8对应volatile acidity所有值的均值接近0.6，综合全部发现volatile acidity值越高，quality值越低 sns.barplot(x='quality',y='alcohol',data=wine) #通过绘图查看均值的分布情况，了解相关性[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-JbQFA3RY-1680958755074)(D:\typora\photo\alcohol.png)] 看到quality为8对应alcohol所有值的均值接近12，综合全部发现alcohol值越高，quality值越高 属性与记录较少，不做数据归约 2.4清洗数据 若有一批新的红葡萄酒数据，通过物理化学测试得知fixed acidity、volatile acid、citric acid、residual sugar、chlorides、free sulfur dioxide、total sulfur dioxide、density 、pH、sulphates、alcohol去求quality 从数据分析得知quality值主要为6个3~8，类别较多，分类难度大，将多分类问题改为二分类问题 可将quality值[3,8]划分为两部分：[3,6]为质量一般，[7,8]质量很好，进行二值化处理，简化问题，判断新酒是属于质量一般/很好 以下程序将quality值进行3分类为low, medium, high print(wine.info()) print(wine.describe()) wine = wine.drop_duplicates() wine['quality'].value_counts().plot(kind = 'pie', autopct = '%.2f') plt.show() print(wine.corr().quality) plt.subplot(121) sns.barplot(x = 'quality', y = 'volatile acidity', data = wine) plt.subplot(122) sns.barplot(x = 'quality', y = 'alcohol', data = wine) plt.show() from sklearn.preprocessing import LabelEncoder bins = (2, 4, 6, 8) #设置待划分数据的bins，bin划分数据的方式为构成左开右闭区间,所以划分结果为(2,4](4,6](6,8] group_names = ['low', 'medium', 'high'] #定义bins划分后的组名 wine['quality_lb'] = pd.cut(wine['quality'], bins = bins, labels = group_names) #使用pandas模块中的cut()函数将数据分箱 属性中多出quality_lb表示最后质量情况 wine Out[15]: fixed acidity volatile acidity ... quality quality_lb 0 7.4 0.700 ... 5 medium 1 7.8 0.880 ... 5 medium 2 7.8 0.760 ... 5 medium 3 11.2 0.280 ... 6 medium 5 7.4 0.660 ... 5 medium 6 7.9 0.600 ... 5 medium 7 7.3 0.650 ... 7 high 8 7.8 0.580 ... 7 high 9 7.5 0.500 ... 5 medium 10 6.7 0.580 ... 5 medium 12 5.6 0.615 ... 5 medium 13 7.8 0.610 ... 5 medium 14 8.9 0.620 ... 5 medium 15 8.9 0.620 ... 5 medium 16 8.5 0.280 ... 7 high 17 8.1 0.560 ... 5 medium 18 7.4 0.590 ... 4 low 19 7.9 0.320 ... 6 medium 20 8.9 0.220 ... 6 medium 21 7.6 0.390 ... 5 medium 22 7.9 0.430 ... 5 medium 23 8.5 0.490 ... 5 medium 24 6.9 0.400 ... 6 medium 25 6.3 0.390 ... 5 medium 26 7.6 0.410 ... 5 medium 28 7.1 0.710 ... 5 medium 29 7.8 0.645 ... 6 medium 30 6.7 0.675 ... 5 medium 31 6.9 0.685 ... 6 medium 32 8.3 0.655 ... 5 medium ... ... ... ... ... 1566 6.7 0.160 ... 6 medium 1568 7.0 0.560 ... 5 medium 1569 6.2 0.510 ... 6 medium 1570 6.4 0.360 ... 6 medium 1571 6.4 0.380 ... 6 medium 1572 7.3 0.690 ... 5 medium 1573 6.0 0.580 ... 6 medium 1574 5.6 0.310 ... 6 medium 1575 7.5 0.520 ... 6 medium 1576 8.0 0.300 ... 6 medium 1577 6.2 0.700 ... 6 medium 1578 6.8 0.670 ... 6 medium 1579 6.2 0.560 ... 5 medium 1580 7.4 0.350 ... 6 medium 1582 6.1 0.715 ... 5 medium 1583 6.2 0.460 ... 5 medium 1584 6.7 0.320 ... 7 high 1585 7.2 0.390 ... 6 medium 1586 7.5 0.310 ... 6 medium 1587 5.8 0.610 ... 6 medium 1588 7.2 0.660 ... 6 medium 1589 6.6 0.725 ... 5 medium 1590 6.3 0.550 ... 6 medium 1591 5.4 0.740 ... 6 medium 1592 6.3 0.510 ... 6 medium 1593 6.8 0.620 ... 6 medium 1594 6.2 0.600 ... 5 medium 1595 5.9 0.550 ... 6 medium 1597 5.9 0.645 ... 5 medium 1598 6.0 0.310 ... 6 medium [1359 rows x 13 columns] 字符串不方便计算，使用preprocessing模块的LabelEncoder()函数分配标签 lb_quality = LabelEncoder() #为quality_lb属性分配标签0，1，2对应low，medium，high wine['label'] = lb_quality.fit_transform(wine['quality_lb']) #label属性为具体标签 属性中出现label表示具体质量登记标签 wine Out[19]: fixed acidity volatile acidity ... quality_lb label 0 7.4 0.700 ... medium 2 1 7.8 0.880 ... medium 2 2 7.8 0.760 ... medium 2 3 11.2 0.280 ... medium 2 5 7.4 0.660 ... medium 2 6 7.9 0.600 ... medium 2 7 7.3 0.650 ... high 0 8 7.8 0.580 ... high 0 9 7.5 0.500 ... medium 2 10 6.7 0.580 ... medium 2 12 5.6 0.615 ... medium 2 13 7.8 0.610 ... medium 2 14 8.9 0.620 ... medium 2 15 8.9 0.620 ... medium 2 16 8.5 0.280 ... high 0 17 8.1 0.560 ... medium 2 18 7.4 0.590 ... low 1 19 7.9 0.320 ... medium 2 20 8.9 0.220 ... medium 2 21 7.6 0.390 ... medium 2 22 7.9 0.430 ... medium 2 23 8.5 0.490 ... medium 2 24 6.9 0.400 ... medium 2 25 6.3 0.390 ... medium 2 26 7.6 0.410 ... medium 2 28 7.1 0.710 ... medium 2 29 7.8 0.645 ... medium 2 30 6.7 0.675 ... medium 2 31 6.9 0.685 ... medium 2 32 8.3 0.655 ... medium 2 ... ... ... ... ... 1566 6.7 0.160 ... medium 2 1568 7.0 0.560 ... medium 2 1569 6.2 0.510 ... medium 2 1570 6.4 0.360 ... medium 2 1571 6.4 0.380 ... medium 2 1572 7.3 0.690 ... medium 2 1573 6.0 0.580 ... medium 2 1574 5.6 0.310 ... medium 2 1575 7.5 0.520 ... medium 2 1576 8.0 0.300 ... medium 2 1577 6.2 0.700 ... medium 2 1578 6.8 0.670 ... medium 2 1579 6.2 0.560 ... medium 2 1580 7.4 0.350 ... medium 2 1582 6.1 0.715 ... medium 2 1583 6.2 0.460 ... medium 2 1584 6.7 0.320 ... high 0 1585 7.2 0.390 ... medium 2 1586 7.5 0.310 ... medium 2 1587 5.8 0.610 ... medium 2 1588 7.2 0.660 ... medium 2 1589 6.6 0.725 ... medium 2 1590 6.3 0.550 ... medium 2 1591 5.4 0.740 ... medium 2 1592 6.3 0.510 ... medium 2 1593 6.8 0.620 ... medium 2 1594 6.2 0.600 ... medium 2 1595 5.9 0.550 ... medium 2 1597 5.9 0.645 ... medium 2 1598 6.0 0.310 ... medium 2 [1359 rows x 14 columns] 用value_counts()方法再统计新类别的分布 wine.label.value_counts() Out[20]: 2 1112 0 184 1 63 Name: label, dtype: int64 对数据进行处理 wine_copy = wine.copy() wine.drop(['quality', 'quality_lb'], axis = 1, inplace = True) #对wine数据属性进行简化，留下label属性 通过数据选择的方式将特征属性和目标属性分开存入X，y X = wine.iloc[:,:-1] #存储特征属性 y = wine.label #存储目标属性 X Out[22]: fixed acidity volatile acidity ... sulphates alcohol 0 7.4 0.700 ... 0.56 9.4 1 7.8 0.880 ... 0.68 9.8 2 7.8 0.760 ... 0.65 9.8 3 11.2 0.280 ... 0.58 9.8 5 7.4 0.660 ... 0.56 9.4 6 7.9 0.600 ... 0.46 9.4 7 7.3 0.650 ... 0.47 10.0 8 7.8 0.580 ... 0.57 9.5 9 7.5 0.500 ... 0.80 10.5 10 6.7 0.580 ... 0.54 9.2 12 5.6 0.615 ... 0.52 9.9 13 7.8 0.610 ... 1.56 9.1 14 8.9 0.620 ... 0.88 9.2 15 8.9 0.620 ... 0.93 9.2 16 8.5 0.280 ... 0.75 10.5 17 8.1 0.560 ... 1.28 9.3 18 7.4 0.590 ... 0.50 9.0 19 7.9 0.320 ... 1.08 9.2 20 8.9 0.220 ... 0.53 9.4 21 7.6 0.390 ... 0.65 9.7 22 7.9 0.430 ... 0.91 9.5 23 8.5 0.490 ... 0.53 9.4 24 6.9 0.400 ... 0.63 9.7 25 6.3 0.390 ... 0.56 9.3 26 7.6 0.410 ... 0.59 9.5 28 7.1 0.710 ... 0.55 9.4 29 7.8 0.645 ... 0.59 9.8 30 6.7 0.675 ... 0.54 10.1 31 6.9 0.685 ... 0.57 10.6 32 8.3 0.655 ... 0.66 9.8 ... ... ... ... ... 1566 6.7 0.160 ... 0.71 11.2 1568 7.0 0.560 ... 0.59 9.2 1569 6.2 0.510 ... 0.57 11.5 1570 6.4 0.360 ... 0.93 12.4 1571 6.4 0.380 ... 0.65 11.1 1572 7.3 0.690 ... 0.51 9.5 1573 6.0 0.580 ... 0.67 12.5 1574 5.6 0.310 ... 0.48 10.5 1575 7.5 0.520 ... 0.64 11.8 1576 8.0 0.300 ... 0.78 10.8 1577 6.2 0.700 ... 0.60 11.9 1578 6.8 0.670 ... 0.67 11.3 1579 6.2 0.560 ... 0.60 11.3 1580 7.4 0.350 ... 0.60 11.9 1582 6.1 0.715 ... 0.50 11.9 1583 6.2 0.460 ... 0.62 9.8 1584 6.7 0.320 ... 0.80 11.6 1585 7.2 0.390 ... 0.84 11.5 1586 7.5 0.310 ... 0.85 11.4 1587 5.8 0.610 ... 0.66 10.9 1588 7.2 0.660 ... 0.78 12.8 1589 6.6 0.725 ... 0.54 9.2 1590 6.3 0.550 ... 0.82 11.6 1591 5.4 0.740 ... 0.56 11.6 1592 6.3 0.510 ... 0.75 11.0 1593 6.8 0.620 ... 0.82 9.5 1594 6.2 0.600 ... 0.58 10.5 1595 5.9 0.550 ... 0.76 11.2 1597 5.9 0.645 ... 0.71 10.2 1598 6.0 0.310 ... 0.66 11.0 [1359 rows x 11 columns] y Out[23]: 0 2 1 2 2 2 3 2 5 2 6 2 7 0 8 0 9 2 10 2 12 2 13 2 14 2 15 2 16 0 17 2 18 1 19 2 20 2 21 2 22 2 23 2 24 2 25 2 26 2 28 2 29 2 30 2 31 2 32 2 .. 1566 2 1568 2 1569 2 1570 2 1571 2 1572 2 1573 2 1574 2 1575 2 1576 2 1577 2 1578 2 1579 2 1580 2 1582 2 1583 2 1584 0 1585 2 1586 2 1587 2 1588 2 1589 2 1590 2 1591 2 1592 2 1593 2 1594 2 1595 2 1597 2 1598 2 Name: label, Length: 1359, dtype: int64 2.5选取训练和测试数据 将数据划分为数据集和训练集，使用train_test_split()函数，该函数可随机地从样本中按比例选取训练数据和测试数据，test_size参数用来设置测试集的比例 from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) #设置测试集比例为20% 2.6标准化处理 数据规范化处理，对特征属性的训练集和测试集用scale()函数进行了标准化处理 from sklearn.preprocessing import scale X_train = scale(X_train) X_test = scale(X_test) 三、机器学习建模 3.1机器学习模型选择 随机森林(Random Forest)是一中机器学习模型，是一种集成学习(Ensemble Learning)算法 集成学习即构建并结合多个学习器来完成学习任务 随机森林模型属于并行式集成学习代表Bagging类型 具体做法为：对原始数据集进行多次随机采样，得到多个不同的采样集，然后基于每个采样集训练一个决策树基学习器，再将这些基学习器进行结合，最终通过投票或取均值等方式使得模型获得较高的精准度和泛化性能 3.2训练模型 利用RandomForestClassifier()函数构建一个分类器，n_estimators参数是指在利用最大投票数或均值来预测前想要建立决策时的子树的数量（因为是基学习器），通常较多的子树，可以让模型有更好的性能 from sklearn.metrics import confusion_matrix rfc = RandomForestClassifier(n_estimators = 200) #构建分类器 rfc.fit(X_train, y_train) #基于训练集进行学习 y_pred = rfc.predict(X_test) #利用predict()方法，基于测试集的X部分数据(X_test)进行预测 print(confusion_matrix(y_test, y_pred)) #将预测效果与实际的y值比较，用常规的混淆矩阵来观察 3.3预测结果判断 混淆矩阵是一种算法性能的可视化呈现，每一列代表预测值，每一行代表实际的类别 #分类结果的混淆矩阵 [[ 16 0 19] #16为类别0（high）判断正确的个数，19为本来是类别0但被误判为类别2，类别0的总个数为16+19 [ 0 1 10] [ 8 1 217]] 对角线上的个数为正确判断出类别的数据记录条数，其他位置为类别误判的条数，对角线上的值占总数越大表示分类效果越好 四、调参 用GridSearchCV去调参需要人工选择的参数成为超参数(随机森林当中决策树的个数即前面的n_estinmators参数对应的值) GridSearchCV函数实则为暴力搜索，将参数输入就可以给出最优化的结果和参数，适用于小数据集 grid_rfc = GridSearchCV(rfc, param_rfc, iid = False, cv = 5) #调参 grid_rfc.fit(X_train, y_train) best_param_rfc = grid_rfc.best_params_ print(best_param_rfc) #保存取得最佳结果的参数的组合 #基于最佳参数组合重新训练模型，预测结果 rfc = RandomForestClassifier(n_estimators = best_param_rfc['n_estimators'], criterion = best_param_rfc['criterion'], random_state=0) rfc.fit(X_train, y_train) y_pred = rfc.predict(X_test) print(confusion_matrix(y_test, y_pred)) {'criterion': 'gini', 'n_estimators': 60} [[ 16 0 30] [ 0 0 13] [ 6 0 207]] 五、整体代码 # -*- coding: utf-8 -*- """ winequality-red data mining @author: Dazhuang """ # url: https://archive.ics.uci.edu/ml/datasets/Wine+Quality import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import GridSearchCV import warnings warnings.filterwarnings('ignore') try: #读取数据 wine = pd.read_csv('winequality-red.csv', sep = ';') #将数据存在wine中，wine为DataFrame对象 except: print("Cannot find the file!") print(wine.info()) print(wine.describe()) wine = wine.drop_duplicates() wine['quality'].value_counts().plot(kind = 'pie', autopct = '%.2f') plt.show() print(wine.corr().quality) plt.subplot(121) sns.barplot(x = 'quality', y = 'volatile acidity', data = wine) plt.subplot(122) sns.barplot(x = 'quality', y = 'alcohol', data = wine) plt.show() from sklearn.preprocessing import LabelEncoder bins = (2, 4, 6, 8) #设置待划分数据的bins，bin划分数据的方式为构成左开右闭区间,所以划分结果为(2,4](4,6](6,8] group_names = ['low', 'medium', 'high'] #定义bins划分后的组名 wine['quality_lb'] = pd.cut(wine['quality'], bins = bins, labels = group_names) #使用pandas模块中的cut()函数将数据分箱 lb_quality = LabelEncoder() #字符串不方便计算，为quality_lb属性分配标签0，1，2对应low，medium，high wine['label'] = lb_quality.fit_transform(wine['quality_lb']) #label属性为具体标签 print(wine.label.value_counts()) wine_copy = wine.copy() wine.drop(['quality', 'quality_lb'], axis = 1, inplace = True) #对wine数据属性进行简化，留下label属性 X = wine.iloc[:,:-1] #存储特征属性 y = wine.label #存储目标属性 from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2) #设置测试集比例为20% from sklearn.preprocessing import scale X_train = scale(X_train) X_test = scale(X_test) from sklearn.metrics import confusion_matrix rfc = RandomForestClassifier(n_estimators = 200) #构建分类器 rfc.fit(X_train, y_train) #基于训练集进行学习 y_pred = rfc.predict(X_test) #利用predict()方法，基于测试集的X部分数据(X_test)进行预测 print(confusion_matrix(y_test, y_pred)) #将预测效果与实际的y值比较，用常规的混淆矩阵来观察 param_rfc = { #选择要调参的参数 "n_estimators": [10,20,30,40,50,60,70,80,90,100,150,200], "criterion": ["gini", "entropy"] } grid_rfc = GridSearchCV(rfc, param_rfc, iid = False, cv = 5) #调参 grid_rfc.fit(X_train, y_train) best_param_rfc = grid_rfc.best_params_ print(best_param_rfc) #保存取得最佳结果的参数的组合 #基于最佳参数组合重新训练模型，预测结果 rfc = RandomForestClassifier(n_estimators = best_param_rfc['n_estimators'], criterion = best_param_rfc['criterion'], random_state=0) rfc.fit(X_train, y_train) y_pred = rfc.predict(X_test) print(confusion_matrix(y_test, y_pred)) 六、参考材料 [1]用Python玩转数据 |
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