from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import roc_auc_score from sklearn.metrics import accuracy_score import datetime from time import time models = [RandomForestClassifier(random_state=123, min_samples_split=3, min_samples_leaf=0.01, max_depth=5), LogisticRegression(random_state=123), SVC(kernel='rbf',gamma='auto',random_state=123,probability=True)] # 训练 for model in models: time0=time() model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) rf_roc_auc = roc_auc_score(y_test,y_pred) print(type(model).__name__, 'accuracy:', accuracy) print('======='*10) print(type(model).__name__, 'roc:', rf_roc_auc) print('======='*10) print(classification_report(y_test, y_pred,target_names=['良性', '恶性'])) print('======='*10)代码解释

这段代码使用了三种分类器(RandomForestClassifier, LogisticRegression, SVC)对数据进行训练和预测，并输出了各自的准确率(accuracy)、ROC曲线下的面积(rf_roc_auc)以及分类报告(classification_report)。

具体解释如下：

1. 导入需要的库和模块：

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
import datetime
from time import time

2. 定义三个分类器模型：

models = [RandomForestClassifier(random_state=123,
                                 min_samples_split=3,
                                 min_samples_leaf=0.01,
                                 max_depth=5),
          LogisticRegression(random_state=123),
          SVC(kernel='rbf',gamma='auto',random_state=123,probability=True)]

其中，RandomForestClassifier是随机森林分类器模型，LogisticRegression是逻辑回归分类器模型，SVC是支持向量机分类器模型。

3. 训练和预测：

for model in models:
    time0=time()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    rf_roc_auc = roc_auc_score(y_test,y_pred)
    print(type(model).__name__, 'accuracy:', accuracy)
    print('======='*10)
    print(type(model).__name__, 'roc:', rf_roc_auc)
    print('======='*10)
    print(classification_report(y_test, y_pred,target_names=['良性', '恶性']))
    print('======='*10)

其中，for循环遍历三个模型，对每个模型进行训练和预测，并输出准确率、ROC曲线下的面积和分类报告。其中，time()函数用于计算训练时间。