[[["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):2024-04-18。"],[[["Random forests utilize out-of-bag (OOB) evaluation, eliminating the need for a separate validation dataset by treating the training set as a test set in a cross-validation-like approach."],["OOB evaluation leverages the fact that each decision tree in the forest is trained on approximately 67% of the training data, allowing the remaining 33% to be used for evaluation, similar to a test set."],["During OOB evaluation, predictions for a specific example are generated using only the decision trees that did not include that example in their training process."],["YDF provides access to OOB evaluation metrics and OOB permutation variable importances within the training logs, offering insights into model performance and feature relevance."]]],[]]