分类和决策树(DT). 决策树是预测建模机器学习的一种重要算法. 决策树模型的表示是二叉树.就是算法和数据结构中的二叉树,没什么特别的.每个节点表示一个单独的输入变量(x)和该变量上的左右孩子(假设变量为数值). 树的叶节点包含一个输出变量(y),用于进行预测.通过遍历树,直到到达叶节点并输出叶节点的类值,就可以做出预测. 树的学习速度很快,预测的速度也很快.它们通常也适用于广泛的问题,不需要对数据进行任何特别的准备. 决策树有很高的方差,并且可以在使用时产生更准确的预测. 特点及应用 决策树的…

This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct ans…

Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees…

Machine Learning Methods: Decision trees and forests This post contains our crib notes on the basics of decision trees and forests. We first discuss the construction of individual trees, and then introduce random and boosted forests. We also discuss…

Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks,and even multioutput tasks. Decision Trees are the fundamental components of Random Forests,which are among the most powerful Machine L…

xgboost的可以参考:https://xgboost.readthedocs.io/en/latest/gpu/index.html 整体看加速5-6倍的样子. Gradient Boosting, Decision Trees and XGBoost with CUDA By Rory Mitchell | September 11, 2017  Tags: CUDA, Gradient Boosting, machine learning and AI, XGBoost   Gradie…

一.Table for Content 在之前的文章中我们介绍了Decision Trees Agorithms,然而这个学习算法有一个很大的弊端,就是很容易出现Overfitting,为了解决此问题人们找到了一种方法,就是对Decision Trees 进行 Pruning(剪枝)操作. 为了提高Decision Tree Agorithm的正确率和避免overfitting,人们又尝试了对它进行集成,即使用多棵树决策,然后对于分类问题投票得出最终结果,而对于回归问题则计算平均结果.下面是几条…

一.Decision Trees Agorithms的简介 决策树算法(Decision Trees Agorithms),是如今最流行的机器学习算法之一,它即能做分类又做回归(不像之前介绍的其他学习算法),在本文中,将介绍如何用它来对数据做分类. 本文参照了Madhu Sanjeevi ( Mady )的Decision Trees Algorithms,有能力的读者可去阅读原文. 说明:本文有几处直接引用了原文,并不是不想做翻译,而是感觉翻译过来总感觉不够清晰,而原文却讲的很明白清晰.(个人…

https://www.quora.com/Why-do-people-use-gradient-boosted-decision-trees-to-do-feature-transform Why is linearity/non-linearity important?Most of our classification models try to find a single line that separates the two sets of point. I say that they…

python机器学习-乳腺癌细胞挖掘(博主亲自录制视频)https://study.163.com/course/introduction.htm?courseId=1005269003&utm_campaign=commission&utm_source=cp-400000000398149&utm_medium=share CatBoost Enables Fast Gradient Boosting on Decision Trees Using GPUs https://d…