写一段逻辑回归算法代码并对其进行优化和评估
时间: 2023-03-15 21:07:48 浏览: 67
首先,我们需要导入必要的库,如NumPy,Pandas等:import numpy as np
import pandas as pd# 然后,加载数据集并将其分割为训练集和测试集:dataset = pd.read_csv('data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values 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, random_state = 0)# 接下来,我们使用sklearn库中的逻辑回归类创建模型:from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)# 最后,我们使用模型对测试集进行预测,并对模型进行优化和评估:y_pred = classifier.predict(X_test) from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy:', accuracy_score(y_test, y_pred))
print('Precision:', precision_score(y_test, y_pred))
print('Recall:', recall_score(y_test, y_pred))
print('F1 Score:', f1_score(y_test, y_pred))# 使用交叉验证来优化模型并获得更好的评估结果from sklearn.model_selection import cross_val_score
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))