New Julia machine learning package: NovaML

CameronBieganek · September 6, 2024, 12:38pm

No, tree is a callable object. It’s actually tree that gets mutated.

Sorry, @ilkerarslan, the bikeshedding has begun. It’s hard to avoid with ML libraries because lots of people are interested and have opinions.

For those who are interested, I already opened an issue regarding the syntax for fitting a model:

github.com/ilkerarslan/NovaML.jl

Alternative syntax for fitting a model

opened 08:32PM - 04 Sep 24 UTC

closed 09:40AM - 05 Sep 24 UTC

CameronBieganek

I don't want to do too much bikeshedding, but it seems to me that `model(X, y)` …and `model(X)` do very different things and they deserve a separate syntax. I like `model(X)` for prediction, since models are just function approximators. But for fitting I would recommend this syntax: ```julia fit!(model, X, y) ``` This syntax makes it more clear that `model` is being mutated during the fitting process, since we are using the standard Julia convention that mutation is indicated by function names with an exclamation point. With the `model(X, y)` syntax it's not very obvious that fitting and mutation are occurring.

I tend to agree with @mirkobunse that immutable data structures lead to clearer code. The LearnAPI.jl Discourse thread discussed possible designs extensively. The two main options (as I see it) for a functional, immutable design are:

Two separate immutable types, one to hold the hyperparameters and one that contains the full fitted object. (The full fitted object would most likely also include the hyperparameters.) So fitting and predicting would look like this:
```
params = RandomForestRegressorParams(n_trees=100)
model = fit(params, X, y)
ŷ = predict(model, Xnew)  # Or `ŷ = model(Xnew)`, as in NovaML and Flux.
```
Have a FittedModel wrapper type that wraps the hyperparameters and the fitted parameters. StatsLearnModels.jl uses this approach.