The final value used for the model was C = 1. Support Vector Machines with Linear Kernel This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for performance, and how to preprocess. The final value used for the model was mtry = 15. RMSE was used to select the optimal model using the smallest value. Resampling results across tuning parameters: Resampling: Cross-Validated (6 fold, repeated 1 times) My_list_model <- function(model,grd=NULL)) In addition, since I didn't need to set up the parameters of lasso, random forest, svmLinear and linear model, how were they tuned by the caret package? Then, I think my real question is: how to configure these different parameters of models, in special the 'nnet' model.
That is, it seems the problem is with the 'nnet' tuning parameters. If I try to throw away the 'nnet' model and change it, for example, to a XGBoost model, in the penultimate line, it seems it works well and results would be calculated. Here I selected different number of nodes in hidden layer and the decay coefficient: my.grid model_listĮrror: The tuning parameter grid should not have columns fractionīy what I understood, I didn't know how to specify very well the tune parameters. However, since each model requires different tuning parameters, I'm in doubt how to set them:įirst I set up the grid to 'nnet' model tunning. To tune some models I intend to use the references of Max Kuhn. The models I select by instance are: Lasso, Random Forest, SVM, Linear Model and Neural Network. My intention is working on a suit of functions that could train the different codes and organize them in a suit of results. I'm trying to implement some functions to compare five different machine learning models to predict some values in a regression problem.
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