决策树与逻辑回归在商业应用中的对比研究

A Comparative Study of Decision Tree and Logistic Regression in Business Applications

分类算法作为常见的机器学习算法为预测分类类别数据提供了强大而又稳定的方式。随着数据挖掘技术的不断进步发展,算法模型的应用场景越来越广泛。在当前数据日益丰富的时代,业者将分类算法应用到商业活动中,打破了传统的营销决策方式,使商业活动变得高效率低成本。然而众多的分类算法都有其特有的理论和逻辑,即便都是用于分类类别数据的方法,其在不同业务需求下的细分应用也会有不同。通过对决策树和逻辑回归两种常用分类算法模型的应用对比研究,得出根据不同的业务需求选择合适的算法模型对达到预期的效果至关重要。

As a common machine learning algorithm,classification algorithm provides a powerful and stable way to predict classification data.With the continuous development of data mining technology,the application scenario of algorithm model is more and more extensive.In the current era of increasingly rich data,operators apply classification algorithm to business activities,breaking the traditional marketing decision-making,so that business activities become efficient and low cost.However,many classification algorithms have their own theories and logic.Even if they are all used for classification data,their application in details under different business requirements will be different.Through the comparative study on the application of decision tree and logistic regression two commonly used classification algorithm models,it is concluded that choosing the appropriate algorithm model according to different business requirements is very important to achieve the desired effect.