Simon Dedman
Simon Dedman
Hastie, T., R. Tibshirani, and J.H. Friedman, 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York gbm.step metrics: self.statistics$correlation = self.cor : runs predict.gbm using...
Abeare: D2 = 1 – (residual deviance/total deviance) cv.statistics$deviance.mean = cv.dev self.statistics$null = total.deviance is now: cv.stats$d.squared in gbm.step.sd
Need to work out how to do these for GLM & GAM still. And re-run the models, e.g. for FI paper.
Already added deviance explained relative to null % in report. gbm.auto v1.5.7
Report to add: - in the BRT combos, round the results: Training Data Correlation: 0.69761649048316 CV Mean Deviance: 0.36294456588329 CV Deviance SE: 0.0144753751984064 CV D squared: 0.999569543999955 CV Mean Correlation:...
Also give option to [add a random variable](https://github.com/SimonDedman/gbm.auto/issues/75) here, if the (an) outcome of gbm.precheck is an edited samples and/or grids. Possibly check samples/grids colnames match Possibly check factorial variables...
So primary aim: take `bin_best_line_temp.csv` and `gaus_best_line_temp.csv` and plot them together as 2 lines (with 2 smoothers?) x range (variable name, first column in csv) will be the same for...
could use the guts of `dismo::gbm.plot`
Could do plots but need csv file equivalents of the Bin_Eval_???.png files in order to agglomerate them
https://www.r-bloggers.com/5-ways-to-measure-running-time-of-r-code/