randomForest(formula = Species ~ ., data = traindata, ntree = 100, proximity = TRUE) Type of random forest: classification Number of trees: 100 No. of variables tried at each split: 2 OOB estimate of error rate: 7.22% Confusion matrix: setosa versicolorvirginicaclass.error setosa 31 0 0...
## randomForest(formula = Species ~ ., data = train, proximity = TRUE) ## Type of random forest: classification ## Number of trees: 500 ## No. of variables tried at each split: 2 ## ## OOB estimate of error rate: 4.95% ## Confusion matrix: ## setosa versicolor virginica class.e...
learner)指那些分类准确率只稍好于随机猜测的分类器(error rate < 50%)。如今集成学习有两个流派,一种是bagging流派,它的特点是各个弱学习器之间没有依赖关系,可以并行拟合,随机森林算法就属于bagging派系;另一个是boosting派系,它的特点是各个弱学习器之间有依赖关系,Adaboost算法就属于boosting派系。在实现集成学习...
## nodesize = 1 OOB error = 32.16% ## nodesize = 2 OOB error = 30.61% ## nodesize = 3 OOB error = 30.69% ## nodesize = 4 OOB error = 29.75% ## nodesize = 5 OOB error = 30.27% ## nodesize = 6 OOB error = 30.87% ## nodesize = 7 OOB error = 28.97% ## nodesize = 8 ...
Random Forest: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm RF的每个树都是随机选择m个样本(有放回的)和n个特征来进行构建,并且不进行减枝。RF的效果也比较依赖于这两个参数。也有人在试验中发现RF的效果也很依赖于随机种子(random_state),对应于sklearn中的 samp_rate,max_features...
randomForest()函数从训练集中有放回地随机抽取84个观测点,在每棵树的每个节点随机抽取36个变量,从而生成了500棵经典决策树。 生成树时没有用到的样本点所对应的类别可由生成的树估计,与其真实类别比较即可得到袋外预测(out-of-bag,OOB)误差,即OOB estimate of error rate,可用于反映分类器的错误率。此处为为1....
七、RandomForest调参示例 Sklearn中集成学习模块 一、XGBoost参数解释 XGBoost的参数一共分为三类: 通用参数:宏观函数控制。 Booster参数:控制每一步的booster(tree/regression)。booster参数一般可以调控模型的效果和计算代价。我们所说的调参,很这是大程度上都是在调整booster参数。
randomForest(formula = Species ~ ., data = iris, importance = TRUE, proximity = TRUE) Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 2 OOB estimate of error rate: 4% Confusion matrix: ...
Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 2 OOB estimate of error rate: 4% Confusion matrix: setosa versicolor virginica class.error setosa 50 0 0 0.00 versicolor 0 47 3 0.06
print(iris.rf)# Call:# randomForest(formula = Species ~ ., data = iris, importance = TRUE, proximity = TRUE)# Type of random forest: classification# Number of trees: 500# No. of variables tried at each split: 2# OOB estimate of error rate: 4.67%# Confusion matrix:# setosa versicolor...