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CitrineInformatics
A random forest
Hi all, I'm interested on applying this RF regressor. I have succesfuly used the RF regressor from scikit learn with my training and testing data set. I have tried to...
How can I save a lolopy model?" I tried to train a model like this: ``` from lolopy.learners import RandomForestRegressor model = RandomForestRegressor() model.fit(X, Y) ``` After that, I attempted...
On training a lolopy `RandomForestRegressor` learner using features that include a feature of type `numpy.bool_`, the resulting model has no signal. Removing the boolean feature or converting it into `numpy.int_`...
[Bagger](https://github.com/CitrineInformatics/lolo/blob/main/src/main/scala/io/citrine/lolo/bags/Bagger.scala#L101-L110) and [MultiTaskBagger](https://github.com/CitrineInformatics/lolo/blob/main/src/main/scala/io/citrine/lolo/bags/MultiTaskBagger.scala#L51-L56) both train the individual models in parallel. Because the order of training is uncontrolled, this means that Lolo random forests are inherently non-reproducible, even if the bagging...
In the latest lolopy version (1.2.0), I fixed random_seed but still, results are not reproducible (I have already fixed numpy random seed). Can you please fix it or tell me...
Are there facilities for sampling from the posterior distribution of the random forest? (e.g. for integration with [Ax](https://ax.dev/docs/bayesopt.html)/[BoTorch](https://botorch.org/)).
Use of multiple JVMs via py4j seems to crash on Linux. See also https://github.com/CitrineInformatics/smlb/issues/70
Despite having a similar name, get_importance_scores is intended to capture the impact of training data, which is related but not identical to the gain scores we use to communicate feature...
Currently `io.citrine.lolo.learners.RandomForest` (and `io.citrine.lolo.learners.ExtraRandomTrees`, which emulates the RF interface) defaults to automatic subset strategy selection when the parameter `subsetStrategy` is an invalid string. This is an opportunity for an unobservable...
getStdDevMean currently uses ~a biased variance estimator~ the square root of the sample variance. This should be unbiased by replacing the denominator with ~`treePredictions.length - 1`~ `treePredictions.length - 1.5` or...