Question

我们可以看到caret::train(..., method = "glmnet")带有交叉验证或cv.glmnet()两者都可以找到最小化交叉验证错误的lambda.min。最终的最佳拟合模型应该是符合lambda.min的模型。那么，为什么我们需要为培训过程设置lambda值的网格？

Answer 1

我们为glmnet模型使用自定义调整网格，因为默认调整网格非常小，我们可能想要探索更多潜在的glmnet模型。

glmnet能够拟合2种不同的惩罚模型，它有2个调整参数：

的阿尔法

岭回归（或alpha = 0）

套索回归（或alpha = 1）

的拉姆达

系数罚分的强度

glmnet模型可以同时适应多个模型（对于单个alpha，lambda的所有值同时适合），我们可以传递大量lambda个值它控制着模型中的惩罚量。

train()足够智能，只能为每个alpha值拟合一个模型，并将所有lambda值传递给一个，以便同时进行拟合。

示例：

# Make a custom tuning grid tuneGrid <- expand.grid(alpha = 0:1, lambda = seq(0.0001, 1, length = 10)) # Fit a model model <- train(y ~ ., overfit, method = "glmnet", tuneGrid = tuneGrid, trControl = myControl ) # Sample Output Warning message: The metric "Accuracy" was not in the result set. ROC will be used instead. + Fold01: alpha=0, lambda=1 - Fold01: alpha=0, lambda=1 + Fold01: alpha=1, lambda=1 - Fold01: alpha=1, lambda=1 + Fold02: alpha=0, lambda=1 - Fold02: alpha=0, lambda=1 + Fold02: alpha=1, lambda=1 - Fold02: alpha=1, lambda=1 + Fold03: alpha=0, lambda=1 - Fold03: alpha=0, lambda=1 + Fold03: alpha=1, lambda=1 - Fold03: alpha=1, lambda=1 + Fold04: alpha=0, lambda=1 - Fold04: alpha=0, lambda=1 + Fold04: alpha=1, lambda=1 - Fold04: alpha=1, lambda=1 + Fold05: alpha=0, lambda=1 - Fold05: alpha=0, lambda=1 + Fold05: alpha=1, lambda=1 - Fold05: alpha=1, lambda=1 + Fold06: alpha=0, lambda=1 - Fold06: alpha=0, lambda=1 + Fold06: alpha=1, lambda=1 - Fold06: alpha=1, lambda=1 + Fold07: alpha=0, lambda=1 - Fold07: alpha=0, lambda=1 + Fold07: alpha=1, lambda=1 - Fold07: alpha=1, lambda=1 + Fold08: alpha=0, lambda=1 - Fold08: alpha=0, lambda=1 + Fold08: alpha=1, lambda=1 - Fold08: alpha=1, lambda=1 + Fold09: alpha=0, lambda=1 - Fold09: alpha=0, lambda=1 + Fold09: alpha=1, lambda=1 - Fold09: alpha=1, lambda=1 + Fold10: alpha=0, lambda=1 - Fold10: alpha=0, lambda=1 + Fold10: alpha=1, lambda=1 - Fold10: alpha=1, lambda=1 Aggregating results Selecting tuning parameters Fitting alpha = 1, lambda = 1 on full training set # Print model to console model # Sample Output glmnet 250 samples 200 predictors 2 classes: 'class1', 'class2' No pre-processing Resampling: Cross-Validated (10 fold) Summary of sample sizes: 225, 225, 225, 225, 224, 226, ... Resampling results across tuning parameters: alpha lambda ROC Sens Spec 0 0.0001 0.3877717 0.00 0.9786232 0 0.1112 0.4352355 0.00 1.0000000 0 0.2223 0.4546196 0.00 1.0000000 0 0.3334 0.4589674 0.00 1.0000000 0 0.4445 0.4718297 0.00 1.0000000 0 0.5556 0.4762681 0.00 1.0000000 0 0.6667 0.4783514 0.00 1.0000000 0 0.7778 0.4826087 0.00 1.0000000 0 0.8889 0.4869565 0.00 1.0000000 0 1.0000 0.4869565 0.00 1.0000000 1 0.0001 0.3368659 0.05 0.9188406 1 0.1112 0.5000000 0.00 1.0000000 1 0.2223 0.5000000 0.00 1.0000000 1 0.3334 0.5000000 0.00 1.0000000 1 0.4445 0.5000000 0.00 1.0000000 1 0.5556 0.5000000 0.00 1.0000000 1 0.6667 0.5000000 0.00 1.0000000 1 0.7778 0.5000000 0.00 1.0000000 1 0.8889 0.5000000 0.00 1.0000000 1 1.0000 0.5000000 0.00 1.0000000 ROC was used to select the optimal model using the largest value. The final values used for the model were alpha = 1 and lambda = 1. # Plot model plot(model)

为什么需要用caret :: train（...，method =“glmnet”）和cv.glmnet（）来调整lambda？

1 个答案: