leaves
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pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
**code:** ``` import ( "fmt" "github.com/dmitryikh/leaves" ) func main() { // 1. Read model useTransformation := true model, err := leaves.LGEnsembleFromFile("lightgbm_model.txt", useTransformation) if err != nil { fmt.Println(err) panic(err) }...
lightgbm changed their [model encoding to v3](https://github.com/microsoft/LightGBM/pull/2269) in `v2.3.0` to support weights in the model. Would like to see `leaves` support this new format.
We use spark to generate libsvm file, then use python sklearn to load it and xgboost to train and save model, finaly use leaves load it and predict. the predict...
I want to get the feature name list in model, does the author have the space to support this feature?  
Just a general question: How complicated would it be to provide the model training part as well? Are there any plans for it?
I use leaves to load my lightgbm model and predict instances, the results are always 0.00, while use python to predict, the result is not. Any one meet the problem...
Hi, As the title said, May leaves would support this type of model, thank for your teams' coding, haha
when I load xgboost model return errs,model is trained for xgboost4j。
leaves does not support the sklearn xgboost model ? I use the below python code to train one xgboost model, meet error when use the below API to load this...
My online prediction service wants to use GBDT + LR (Practical Lessons from Predicting Clicks on Ads at Facebook) algorithm combination, It will use the leaf index of tree. But...