Logistic-Regression:我对Logistic回归的实现并将其应用于不同的数据集

上传者: 42097668 | 上传时间: 2025-06-08 12:33:03 | 文件大小: 283KB | 文件类型: ZIP
逻辑回归 此存储库包含我对Logistic回归的实现,以及将其应用于不同数据集的示例,并解释了有关数据预处理步骤和学习算法行为的每个示例。 。 。 在完成了由Andrew Ng教授的deeplearning.ai的神经网络和深度学习课程之后,我制作了此回购协议,将logistic回归应用于不同的数据集,以更好地理解算法及其工作原理。 在Coursera上, 。 什么是逻辑回归? Logistic回归是一种用于二进制分类问题的监督学习技术,其中数据集包含一个或多个确定二进制结果(0或1)的独立变量。 在逻辑回归分类器中,您可能想要输入描述单个数据行的特征的特征向量X,并且要预测二进制输出值0或1。 更正式地说,给定输入向量X,您要预测y_hat,它是一个输出向量,描述给定特征向量X y = 1的概率, y_hat = p(y = 1 / X) 。 例如: 您有一个输入向量X,其特征是

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