RandomForestClassifier的参数oob_score要设置为True(默认为False) RandomForestClassifier的参数bootstrap要设置为True(默认为True) 通过访问RandomForestClassifier对象的实例变量oob_score_就能得到1-oob_error的值 4. 总结 本文描述了参考1中对RandomForest的关键部分out-of-bag (oob) error estimate的理解,也给出了参...
"OOB误差率"是指在随机森林(Random Forest)模型中的袋外误差率(Out-of-Bag Error Rate)。随机森林是一种集成学习算法,它通过整合多个决策树的预测结果来提高模型的性能。 在随机森林中,每个决策树的训练数据是通过有放回抽样(Bootstrap Sampling)得到的,这导致一部分数据没有被用于训练每个树。这未被用于训练的...
https://www.analyticsvidhya.com/blog/2020/12/out-of-bag-oob-score-in-the-random-forest-algorithm/
Random Forest实现中,大多数内部对象是私有(private[tree])的,所以扩展代码使用了org.apache.spark.mllib.tree的命名空间,复用这些内部对象。实现过程中,需要放回抽样数据,Random Forest的原始实现是放在局部变量baggedInput中,外部无法访问,所以扩展代码必须冗余部分原始代码,用于访问baggedInput变量。原始实现中,会先将La...
forest: RandomForestModel, oobError: Double): Array[Double] = { (0untilbins.size).par.map(featureIndex => { val binCount = if (strategy.categoricalFeaturesInfo.contains(featureIndex)) { // category feature strategy.categoricalFeaturesInfo(featureIndex) ...
(formula = Event ~ ., data = trainData, importance = TRUE, ntree = 500)# Type of random forest: classification# Number of trees: 500# No. of variables tried at each split: 6## OOB estimate oferrorrate: 22.4%# Confusion...
I'm just looking for a general understanding of why the order of predictors in a randomForest can affect the OOB estimate. I think I might know the answer, but am not sure. I'm guessing it has to do with the way the trees are built (starting at the first predictor and branc...
RandomForest 随机森林 多棵决策树组成的集成学习模型 原理:东写西读:机器学习超详细实践攻略(10):...
Simulation studies are performed to compare the effect of the input parameters on the predictive ability of the random forest. The number of variables sampled, m-try, has the largest impact on the true prediction error. It is often claimed that the out-of-bag error (OOB) is an unbiased ...
我们可以这样⽐比喻随机森林算法:每一棵决策树就是一个精通于某一个窄领域的专家(因为我们 从M个...