1.设置一个非二元分类问题 2.进行特征选择 3.使用并比较几个不同的分类器进行分类 4.应用各种指标来评估分类质量。 请用python写这些代码

好的，我会根据您的要求为您编写Python代码来完成这些任务。以下是完整的代码：

# 导入必要的库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

# 读取数据集
data = pd.read_csv('dataset.csv')

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)

# 进行特征选择
selector = SelectKBest(chi2, k=10)
selector.fit(X_train, y_train)
X_train_selected = selector.transform(X_train)
X_test_selected = selector.transform(X_test)

# 使用不同的分类器进行分类
clf1 = MultinomialNB()
clf2 = RandomForestClassifier(n_estimators=100)
clf3 = SVC(kernel='linear')

# 训练分类器
clf1.fit(X_train_selected, y_train)
clf2.fit(X_train_selected, y_train)
clf3.fit(X_train_selected, y_train)

# 在测试集中进行预测
y_pred1 = clf1.predict(X_test_selected)
y_pred2 = clf2.predict(X_test_selected)
y_pred3 = clf3.predict(X_test_selected)

# 计算准确度
acc1 = accuracy_score(y_test, y_pred1)
acc2 = accuracy_score(y_test, y_pred2)
acc3 = accuracy_score(y_test, y_pred3)

# 计算混淆矩阵
cm1 = confusion_matrix(y_test, y_pred1)
cm2 = confusion_matrix(y_test, y_pred2)
cm3 = confusion_matrix(y_test, y_pred3)

# 计算分类报告
cr1 = classification_report(y_test, y_pred1)
cr2 = classification_report(y_test, y_pred2)
cr3 = classification_report(y_test, y_pred3)

# 打印结果
print('MultinomialNB Accuracy:', acc1)
print('MultinomialNB Confusion Matrix:\n', cm1)
print('MultinomialNB Classification Report:\n', cr1)

print('RandomForestClassifier Accuracy:', acc2)
print('RandomForestClassifier Confusion Matrix:\n', cm2)
print('RandomForestClassifier Classification Report:\n', cr2)

print('SVM Accuracy:', acc3)
print('SVM Confusion Matrix:\n', cm3)
print('SVM Classification Report:\n', cr3)

请注意，这是一个示例代码，并且需要从您的数据集中进行修改才能正常运行。