gbm | 易学教程

(R) Axis widths in gbm.plot

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问题 Hoping for some pointers or some experiences insight as i'm literally losing my mind over this, been trying for 2 full days to set up the right values to have a function spit out clean simple line plots from the gbm.plot function (packages dismo & gbm). Here's where I start. bty=n in par to turn off the box & leave me with only left & bottom axes. Gbm.plot typically spits out one plot per explanatory variable, so usually 6 plots etc, but I'm tweaking it to do one per variable & looping it. I

(R) Axis widths in gbm.plot

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GBM multinomial distribution, how to use predict() to get predicted class?

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问题 I am using the multinomial distribution from the gbm package in R. When I use the predict function, I get a series of values: 5.086328 -4.738346 -8.492738 -5.980720 -4.351102 -4.738044 -3.220387 -4.732654 but I want to get the probability of each class occurring. How do I recover the probabilities? Thank You. 回答1: Take a look at ?predict.gbm , you'll see that there is a "type" parameter to the function. Try out predict(<gbm object>, <new data>, type="response") . 回答2: predict.gbm(..., type=

generic buffer manager(gbm)

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1. 参考link https://chromium.googlesource.com/chromiumos/platform/minigbm/+/refs/heads/master/ http://virtuousgeek.org/blog/index.php/jbarnes/2011/10/31/writing_stanalone_programs_with_egl_and_ https://blog.csdn.net/weixin_42263483/article/details/100576577 https://github.com/robclark/libgbm 2.gbm是一种硬件抽象的buffer manager管理库 3.gbm 中的一些概念跨距-stride：其实是为了提升CPU访问内存性能，而做的内存对齐；图像操作经常是按行操作，stride 就是指图像中的一行图像数据所占的存储空间的长度，它是一个大于等于图像宽度的内存对齐的长度来源： CSDN 作者： lkjxcj3096 链接： https://blog.csdn.net/lkjxcj3096/article/details/103744376

Reproduce predictions with MOJO file of a H2O GBM model

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问题 I used H2O version 3.26.0.5 to train a GBM model in a binary problem, to predict the probability of positive class. I saved the model file as MOJO and used this file to generate predictions in new data: ## first, restart R session ## # load the model library(h2o) h2o.init(nthreads = -1) model <- h2o.import_mojo("path_to_mojo_file") # load the new data input input <- read_csv("path_to_new_data") input_h2o <- as.h2o(input) # predictions predictions <- predict(model, input_h2o) When I run this

Y axes on the logit scale and centered in gbm.plot

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问题 I am currently exploring the gbm functions in the package dismo to create boosted regression trees for species distribution modeling. I have been using the dismo vignettes as well as the 2008 paper "A working guide to boosted regression trees" by Elith et al., published in the Journal of Animal Ecology. On page 808:809 of the Elith et al. article, the authors explain partial dependence plots and give an example at the bottom of page 809 (Fig. 6). According to the dismo vignette "Boosted

Reproducibility of results from predict() function - raster package

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问题 I am having trouble reproducing my results exactly when I make predictions with predict() and a saved gbm model object. I am using the raster package to predict to a raster. Each time I run predict() with the same model object and inputs (a raster stack), I get slightly different values (max value within 0.7 for a range of predictions from 0.08 to 12.30 for example). However, there seems to be a limited amount of outcomes. For example, I can get the results to reproduce if I run predict

How to get different Variable Importance for each class in a binary h2o GBM in R?

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问题 I'm trying to explore the use of a GBM with h2o for a classification issue to replace a logistic regression (GLM). The non-linearity and interactions in my data make me think a GBM is more suitable. I've ran a baseline GBM (see below) and compared the AUC against the AUC of the logistic regression. THe GBM performs much better. In a classic linear logistic regression, one would be able to see the direction and effect of each of the predictors (x) on the outcome variable (y). Now, I would like

In gbm multinomial dist, how to use predict to get categorical output? [duplicate]

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问题 This question already has answers here : GBM multinomial distribution, how to use predict() to get predicted class? (2 answers) Closed 4 years ago . My response is a categorical variable (some alphabets), so I used distribution='multinomial' when making the model, and now I want to predict the response and obtain the output in terms of these alphabets, instead of matrix of probabilities. However in predict(model, newdata, type='response') , it gives probabilities, same as the result of type=

Caret train method complains Something is wrong; all the RMSE metric values are missing

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问题 On numerous occasions I've been getting this error when trying to fit a gbm or rpart model. Finally I was able to reproduce it consistently using publicly available data. I have noticed that this error happens when using CV (or repeated cv). When I don't use any fit control I don't get this error. Can some shed some light one why I keep getting error consistently. fitControl= trainControl("repeatedcv", repeats=5) ds = read.csv("http://www.math.smith.edu/r/data/help.csv") ds$sub = as.factor(ds