统计观点(Statistical View), 以Friedman, Hastie & Tibshirani为代表,Additive logistic regression: A statistical view of boosting (with discussions). Annals of Statistics, 28(2):337–407, 2000, 简称FHT00 间隔理论在支持向量机等算法有着广泛使用,但是统计学派却不承认有“间隔”这么一个统计量,想想那个时...
GBDT V.S. LR(Linear Regression? Logistic Regression?) 从决策边界来说,线性回归的决策边界是一条直线,逻辑回归的决策边界根据是否使用核函数可以是一条直线或者曲线,而GBDT的决策边界可能是很多条线。 逻辑回归算法在某一数据集上得到的决策边界。 来源:Andrew Ng在Coursera上机器学习的讲义。 GBDT并不一定总是好...
returndataMat,labelMat 可以看到,最终使用AdaBoost进行预测所得结果的错误率约为26%,而之前其实对这个数据集我们也使用logistic regression进行过预测,平均错误率为35%左右,可以看到AdaBoost的准确率是高于logistic Regression的
Logit和Gentle算法的提出过程大致是这样的 1. 验证Boosting algorithms是一种拟合一个additive logistic regression model(加性的逻辑回归模型)的阶段式估计过程.它有最优一个指数判据,这个判据由第二定理与二项式对数似然判据是等价的. 2. 作者证明Discrete是使用adaptive Newton updates拟合一个additive logistic regression...
logistic regressionData mining classification techniques are affected by the presence of imbalances between classes of a response variable. The difficulty in handling the imbalanced data issue has led to an influx of methods, either resolving the imbalance issue at data or algorithmic level. The R ...
1. 验证Boosting algorithms是一种拟合一个additive logistic regression model(加性的逻辑回归模型)的阶段式估计过程.它有最优一个指数判据,这个判据由第二定理与二项式对数似然判据是等价的. 2. 作者证明Discrete是使用adaptive Newton updates拟合一个additive logistic regression model来最小化Ee^(-yF(x))的过程,...
1.验证Boosting algorithms是一种拟合一个additive logistic regression model(加性的逻辑回归模型)的阶段式估计过程. 它有最优一个指数判据,这个判据由第二定理与二项式对数似然判据是等价的. 2.作者证明Discrete是使用adaptive Newton updates拟合一个additive logistic regression model来最小化Ee^(-yF(x))的过程, ...
GBDT V.S. LR(Linear Regression? Logistic Regression?) 从决策边界来说,线性回归的决策边界是一条直线,逻辑回归的决策边界根据是否使用核函数可以是一条直线或者曲线,而GBDT的决策边界可能是很多条线。 逻辑回归算法在某一数据集上得到的决策边界。来源:Andrew Ng在Coursera上机器学习的讲义。
but when we group multiple weak classifiers with each one progressively learning from the others’ wrongly classified objects, we can build one such strong model. The classifier mentioned here could be any of your basic classifiers, from Decision Trees (often the default) to Logistic Regression, ...
but when we group multiple weak classifiers with each one progressively learning from the others’ wrongly classified objects, we can build one such strong model. The classifier mentioned here could be any of your basic classifiers, from Decision Trees (often the default) to Logistic Regression, ...