我在尝试解决Kaggle上的泰坦尼克号问题（https://www.kaggle.com/c/titanic）。我试图使用sklearn.preprocessing库中的LabelEncoder和OneHotEncoder类对”Sex”列进行分类编码。以下是我的代码：

# 导入数据分析库import pandas as pdimport numpy as npimport random as rnd# 导入数据可视化库import seaborn as snsimport matplotlib.pyplot as plt# 导入机器学习库from sklearn.linear_model import LogisticRegressionfrom sklearn.svm import SVC, LinearSVCfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.naive_bayes import GaussianNBfrom sklearn.linear_model import Perceptronfrom sklearn.linear_model import SGDClassifierfrom sklearn.tree import DecisionTreeClassifier# 获取数据集train = pd.read_csv('./input/train.csv')test = pd.read_csv('./input/test.csv')combine = [train, test]# 特征可视化g = sns.FacetGrid(train, col='Survived')g.map(plt.hist, 'Age', bins=20)grid = sns.FacetGrid(train, col='Survived', row='Pclass', size=2.2, aspect=1.6)grid.map(plt.hist, 'Age', alpha=.5, bins=20)grid.add_legend()g = sns.FacetGrid(train, col='Survived')g.map(plt.hist, 'Parch', bins=20)g = sns.FacetGrid(train, col='Survived')g.map(plt.hist, 'SibSp', bins=20)g = sns.FacetGrid(train, col='Survived')g.map(plt.hist, 'Fare', bins=20)g = sns.FacetGrid(train, col='Survived')g.map(plt.hist, 'Sex', bins=20)# 处理缺失值train.fillna(train.median(), inplace = True)# 对Embarked和Sex特征进行分类# train['Embarked'] = train['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} )# train['Sex'] = train['Sex'].map( {'male': 0, 'female': 1} )# 数据预处理X_train = train.iloc[:, [0, 2, 4, 5, 6, 7, 9]].valuesy_train = train.iloc[:, [1]].valuesX_test  = test.iloc[:, [1, 3, 4, 5, 6, 8]].valuesfrom sklearn.preprocessing import Imputer, LabelEncoder, OneHotEncoder, StandardScalerlabelencoder_X=LabelEncoder()X_train[:, 0]=labelencoder_X.fit_transform(X_train[:, 0])onehotencoder=OneHotEncoder(categorical_features=[0])X_train=onehotencoder.fit_transform(X_train).toarray()

当我执行最后5行代码时，出现了以下错误：

Traceback (most recent call last):  File "<ipython-input-58-770fc19a6644>", line 5, in <module>    X_train=onehotencoder.fit_transform(X_train).toarray()  File "C:\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 2019, in fit_transform    self.categorical_features, copy=True)  File "C:\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py", line 1809, in _transform_selected    X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)  File "C:\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 433, in check_array    array = np.array(array, dtype=dtype, order=order, copy=copy)ValueError: could not convert string to float: 'male'

我的错误是什么？有没有其他有效的编码分类数据的技术？

回答：

OneHotEncoder期望接收整数值——这就是它对'male'（字符串）值提出抱怨的原因。

你可以先使用LabelEncoder将非数值值编码成数字，然后再应用OneHotEncoder

或者使用LabelBinarizer对单个非数值列进行OneHot编码