The R package tfestimators (https://tensorflow.rstudio.com/tfestimators/) lists several canned estimators currently available:
linear_regressor() Linear regressor model.
linear_classifier() Linear classifier model.
dnn_regressor() DNN Regression.
dnn_classifier() DNN Classification.
dnn_linear_combined_regressor() DNN Linear Combined Regression.
dnn_linear_combined_classifier() DNN Linear Combined Classification.
There is mention of SVMs and random forests "coming soon". Does anyone know of a way to implement SVMs and random forests in tensorflow through R at this time?
Thanks very much!
Related
I've been using R for only a year so I don't have much experience, as part of a research project, we are using a random intercept logistic regression model and we would like to do a k-fold validation. For our fixed model we were using the caret package without issue but the train() function doesn't seem to have the possibility of including a random intercept.
Has anyone done a k-fold validation for a random intercept log-reg successfully with the caret package, and if not, what other methods have you used?
Thank you so much!!
jon
I'm trying to learn about MARS/Earth models for classification and am using "classif.earth" in the MLR package in R. My issue is that the MLR documentation says that "classif.earth" performs flexible discriminant analysis using the earth algorithm.
However, when I look at the code:
(https://github.com/mlr-org/mlr/blob/master/R/RLearner_classif_earth.R)
I don't see a call to fda in the mda package, rather it directs earth to fit a glm with a default logit link.
So tell me if I'm wrong, but it seems to me that "classif.earth" is not doing flexible discriminant analysis but rather fitting a logistic regression on the earth model.
The implementation uses MARS to perform the FDA, where the MARS model determines the different groups. You can find more information in this paper; I quote from the abstract:
Linear discriminant analysis is equivalent to multiresponse linear regression [...] to represent the groups.
I have made SVM model using SVM package in R for a classification problem. I got only 87% accuracy. But random forest produces around 92.4%.
fit.svm<-svm(modelformula, data=training, gamma = 0.01, cost = 1,cross=5)
Would like to use boosting for tuning this SVM model. Can someone will help me to tune this SVM model?
What are the best parameters I can provide for SVM method?
Example for booting for SVM model.
To answer your first question.
The e1071 library in R has a built-in tune() function to perform CV. This will help you select the optimal parameters cost, gamma, kernel. You can also manipulate a SVM in R with the package kernlab. You may get different results from the 2 libraries. Let me know if you need any examples.
You may want to look into the caret package. It allows you to both pick various kernels for SVM (model list) and also run parameter sweeps to find the best model.
I can see how cv.glm work with a glm object, but what about fitted survival models?
I have a bunch of models (Weibull, Gompertz, lognormal, etc). I want to assess the prediction error using cross validation. Which package/function can do this in R?
SuperLearner can do V-fold cross-validation for a large library of underlying machine learning algorithms, not sure that it includes survival models. Alternatively, take a look at the cvTools package, which is designed to help do cross-validation of any prediction algorithm you give it.
I've been using the caret package in R to run some boosted regression tree and random forest models and am hoping to generate prediction intervals for a set of new cases using the inbuilt cross-validation routine.
The trainControl function allows you to save the hold-out predictions at each of the n-folds, but I'm wondering whether unknown cases can also be predicted at each fold using the built-in functions, or whether I need to use a separate loop to build the models n-times.
Any advice much appreciated
Check the R package quantregForest, available at CRAN. It can easily calculate prediction intervals for random forest models. There's a nice paper by the author of the package, explaining the backgrounds of the method. (Sorry, I can't say anything about prediction intervals for BRT models; I'm looking for them by myself...)