决策森林是一个泛指术语,用于描述由多个决策树组成的模型。决策森林的预测结果是其决策树预测结果的聚合。这种聚合的实现取决于用于训练决策森林的算法。例如,在多类别分类随机森林(一种决策森林)中,每个树为单个类别投票,随机森林预测结果是最具代表性的类别。在二元分类梯度提升树 (GBT)(另一种决策森林)中,每个树都会输出一个对数(浮点值),并且梯度提升树预测是这些值后跟激活函数(例如 S 型函数)的总和。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2022-09-27。"],[[["Decision forests encompass models composed of multiple decision trees, with predictions derived from aggregating individual tree predictions."],["Prediction aggregation methods vary depending on the specific decision forest algorithm employed, such as voting in random forests or logit summation in gradient boosted trees."],["Random forests and gradient boosted trees are two primary examples of decision forest algorithms, each utilizing a unique approach to prediction aggregation."],["Upcoming chapters will delve deeper into the workings of random forests and gradient boosted trees."]]],[]]