Comments (3)
Hi David,
I am really not familiar with PLSRegression so it's hard to have any though on the implementation. If it is only linear, it shouldn't be very difficult. You could look at the implementation of linear regression that is a few lines and I can help explaining.
(I was planning on adding a Section about how to implement additional models in the documentation but I didn't get to it and probably won't this year).
Regarding accepting PRs, in theory, yes I am very happy to.
In practice we haven't setup yet the contributor license agreement so for final acceptance we'd have to wait for this.
from gurobi-machinelearning.
Sounds good, I'll get started then. What's the estimated timeline for the CLA?
from gurobi-machinelearning.
Sounds good, I'll get started then. What's the estimated timeline for the CLA?
I don't know, it's not in my hands.
Certainly, if we have someone waiting in the queue, we can put pressure and the CLA will arrive faster :-)
from gurobi-machinelearning.
Related Issues (20)
- Tests should generate the predictor they need if the file is not there HOT 1
- Support pandas input, handle better fixed features
- Explain better the source of infeasibilites w.r.t. predictor in decision tree models
- Speed up model generation for decision trees HOT 1
- Make model generation faster for nested formulations
- [feature request] support paddle HOT 1
- Type of variables for classication
- BUG: local variable 'trans_constr' referenced before assignment HOT 1
- Missing tests for column transformer
- Add support for XGBoost regressors HOT 2
- Issues in fixed formulation test when it fails
- Support xgboost sklearn API in sklearn pipelines
- [feature request] Support sklearn monotonic constraints HOT 1
- XGBoost constraints don't work with verbose mode HOT 2
- [Feature request] Provide lower and upper bounds to intermediate neurons HOT 6
- Accept list of lists of variables as argument to add_pred_constr
- Support for LightGBM HOT 1
- Bug with a column transformer and empty list of columns
- Remove dependency on scikit-learn
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