# EvoTrees.jl v0.15.0
**URL:** https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849
**Category:** Package Announcements
**Tags:** machine-learning
**Created:** [June 26, 2023, 3:25pm UTC](https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849 "2023-06-26T15:25:40Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@jeremiedb](https://discourse.julialang.org/u/jeremiedb)
#### Post date: [June 26, 2023, 3:25pm UTC](https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849/1 "2023-06-26T15:25:40Z")
EvoTrees.jl just went through a significant refurbishment for v0.15.0:
> **[GitHub - Evovest/EvoTrees.jl: Boosted trees in Julia](https://github.com/Evovest/EvoTrees.jl)**
>
> Boosted trees in Julia
> **[Introduction · EvoTrees.jl](https://evovest.github.io/EvoTrees.jl/stable/)**
>
> Documentation for EvoTrees.jl.
### Direct handling of `Tables` compatible data
It’s not possible to train directly from Tables like structure, most notably DataFrame and named tuples, which are natural ways in which tabular data presents itself:
```julia
using EvoTrees, DataFrames
config = EvoTreeRegressor()
dtrain = DataFrame(x_train, :auto)
dtrain.y .= y_train
m = fit_evotree(config, dtrain; target_name="y")
pred = m(dtrain)
```
Support for the original Matrix/Vector based data remains:
```julia
x_train, y_train = rand(1_000, 10), rand(1_000)
m = fit_evotree(config; x_train, y_train)
pred = m(x_train)
```
### Handling of Categorical and Bool types
When using a `Table` compatible data input, features with element types `Real` (incl. `Bool`) and `Categorical` are automatically recognized as input features. Alternatively, `fnames` kwarg can be used to explicitly specify feature vars.
```julia
m = fit_evotree(config, dtrain; target_name="y", fnames=["x1", "x3"]);
```
`Categorical` features are treated accordingly by the algorithm. Ordered variables will be treated as numerical features, using `≤` split rule, while unordered variables are using `==`. Support is currently limited to a maximum of 255 levels. `Bool` variables are treated as unordered, 2-levels cat variables.
### Improved handling of devices (CPU/GPU)
GPU memory footprint has been significantly reduced thanks to a single histogram kept on GPU ram instead of 3 for every node of a tree.
Training on “cpu” or “gpu” is now controlled over the `kwarg` passed to `fit_evotree` (no longer part of the model contructor such as `EvoTreeRegressor`.
All GPU specific `structs` have been removed, common CPU based structs are used for both CPU and GPU based training (GPU specific objects are kep in cache).
### Fixed numerical instabilities
EvoTree model contructors used to support the `T` kwarg to specify either `Float32` or `Float64` as the basis for computation, ex: `EvoTreeRegressor(T=Float64)`. This has been dropped in v0.15 and instead calculations at the observation level are handled as `Float32` while accumulations are done with `Float64`. This provides best of both world: it solves some numerical instabilities observed with `Float32` on some larger datasets, while keeping performance similar to full `Float32` precision.
---
### Author:  [@mrufsvold](https://discourse.julialang.org/u/mrufsvold)
#### Post date: [June 26, 2023, 8:25pm UTC](https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849/2 "2023-06-26T20:25:00Z")
This package is amazing! The docs are fabulous.
Out of curiosity – is there a particular reason it’s not been registered?
---
### Author:  [@jeremiedb](https://discourse.julialang.org/u/jeremiedb)
#### Post date: [June 26, 2023, 8:27pm UTC](https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849/3 "2023-06-26T20:27:32Z")
Thanks for the kind words!
> [@mrufsvold](#):
>
> Out of curiosity – is there a particular reason it’s not been registered?
EvoTrees.jl is indeed registered: [https://github.com/JuliaRegistries/General/tree/master/E/EvoTrees](https://github.com/JuliaRegistries/General/tree/master/E/EvoTrees).
Have you encountered some issue installing from General registry?
---
### Author:  [@mrufsvold](https://discourse.julialang.org/u/mrufsvold)
#### Post date: [June 26, 2023, 9:26pm UTC](https://discourse.julialang.org/t/evotrees-jl-v0-15-0/100849/4 "2023-06-26T21:26:38Z")
Oh, great! I didn’t try, just saw that the docs pointed to the GitHub url. Thanks!
Edit: just read the “Latest” header. Dunno how I missed that 🤦