Julia下的混合集成学习包：Orchestra
jopen
10年前
Orchestra是Julia编程语言的一个异构集成学习包。它由一个统一的机器学习API驱动，是Julia下对Scikit-Learn和Carret的统一。
入门
We will cover how to predict on a dataset using Orchestra.
获取数据
A tabular dataset will be used to obtain our instances and labels.
This will be split it into a training and test set using holdout method.
import RDatasets using Orchestra.Util using Orchestra.Transformers # Obtain instances and labels dataset = RDatasets.dataset("datasets", "iris") instances = array(dataset[:, 1:(end-1)]) labels = array(dataset[:, end]) # Split into training and test sets (train_ind, test_ind) = holdout(size(instances, 1), 0.3) Create a Learner
A transformer processes instances in some form. Coincidentally, a learner is a subtype of transformer.
A transformer can be created by instantiating it, taking an options dictionary as an optional argument.
All transformers, including learners are called in the same way.
# Learner with default settings learner = PrunedTree() # Learner with some of the default settings overriden learner = PrunedTree({ :impl_options => { :purity_threshold => 0.5 } }) # All learners are called in the same way. learner = StackEnsemble({ :learners => [ PrunedTree(), RandomForest(), DecisionStumpAdaboost() ], :stacker => RandomForest() })