[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2025-02-25 UTC"],[[["\u003cp\u003eRandom 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.\u003c/p\u003e\n"],["\u003cp\u003eOOB 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.\u003c/p\u003e\n"],["\u003cp\u003eDuring OOB evaluation, predictions for a specific example are generated using only the decision trees that did not include that example in their training process.\u003c/p\u003e\n"],["\u003cp\u003eYDF provides access to OOB evaluation metrics and OOB permutation variable importances within the training logs, offering insights into model performance and feature relevance.\u003c/p\u003e\n"]]],[],null,["\u003cbr /\u003e\n\nOut-of-bag evaluation\n\nRandom forests do not require a validation dataset. Most random forests use a\ntechnique called **out-of-bag-evaluation** (**OOB** **evaluation**) to evaluate\nthe quality of the model. OOB evaluation treats the training set as if it were\non the test set of a cross-validation.\n\nAs explained earlier, each decision tree in a random forest is typically trained\non \\~67% of the training examples. Therefore, each decision tree does not see\n\\~33% of the training examples. The core idea of OOB-evaluation is as follows:\n\n- To evaluate the random forest on the training set.\n- For each example, only use the decision trees that did not see the example during training.\n\nThe following table illustrates OOB evaluation of a random forest with 3\ndecision trees trained on 6 examples. (Yes, this is the same table as in\nthe Bagging section). The table shows which decision tree is used with\nwhich example during OOB evaluation.\n\n**Table 7. OOB Evaluation - the numbers represent the number of times a given\ntraining example is used during training of the given example**\n\n| | Training examples |||||| Examples for OOB Evaluation |\n| | #1 | #2 | #3 | #4 | #5 | #6 | |\n| original dataset | 1 | 1 | 1 | 1 | 1 | 1 |\n| decision tree 1 | 1 | 1 | 0 | 2 | 1 | 1 | #3 |\n| decision tree 2 | 3 | 0 | 1 | 0 | 2 | 0 | #2, #4, and #6 |\n| decision tree 3 | 0 | 1 | 3 | 1 | 0 | 1 | #1 and #5 |\n|------------------|----|----|----|----|----|----|-----------------------------|\n\nIn the example shown in Table 7, the OOB predictions for training example 1\nwill be computed with decision tree #3 (since decision trees #1 and #2 used\nthis example for training). In practice, on a reasonable size dataset and\nwith a few decision trees, all the examples have an OOB prediction. \nYDF Code\nIn YDF, the OOB-evaluation is available in the training logs if the model is trained with `compute_oob_performances=True`.\n\nOOB evaluation is also effective to compute permutation variable importance for\nrandom forest models. Remember from [Variable\nimportances](/machine-learning/decision-forests/variable-importances) that\npermutation variable importance measures the importance of a variable by\nmeasuring the drop of model quality when this variable is shuffled. The random\nforest \"OOB permutation variable importance\" is a permutation variable\nimportance computed using the OOB evaluation. \nYDF Code\nIn YDF, the OOB permutation variable importances are available in the training logs if the model is trained with `compute_oob_variable_importances=True`."]]
