We implemented two methods of ensemble pruning 6. Both perform predictions through majority voting. We are then in possession of a jungle of size 10,000 decision trees, and a super-ensemble of size 100 RFs. To clarify, assume we perform 100 runs of RFs of size 100. Use an ensemble of ensembles, namely an ensemble of RFs, with prediction done through majority voting, where each RF votes for a single class. Use all collected fitted models to form class predictions through majority voting, where each model votes for a single class. Once in possession of a collection of fitted models it is time to produce a final ensemble. Herein, we will collect models saved over multiple runs of RF training. Upon describing the setup (“ Experimental setup”), we show promising results (“ Results”), followed by a discussion (“ Discussion”) and concluding remarks (“ Concluding remarks”).Ĭonservation ML begins with amassing a collection of models-through whatever means. Herein, focusing on classification tasks, we perform extensive experimentation involving 5 cultivation methods, including the newly introduced lexigarden (“ Ensemble cultivation”), 6 dataset sources, and 31 datasets (“ Datasets”). In 1 we offered a discussion and a preliminary proof-of-concept of conservation ML, involving a single dataset source, 10 datasets, and a single so-called cultivation method. Our second contribution in this paper is the introduction of a new ensemble cultivation method- lexigarden. We advocate making all models available to everyone, thus having conservation live up to its name, furthering the cause of data and computational science. Most of these models would be discarded unflinchingly, with only a minute handful retained, and possibly reported upon in the literature. It would not be hard to imagine that the number of models produced over time would run into the millions (quite easily more). Consider the common case wherein several research groups have been tackling an extremely hard problem (e.g., 7), each group running variegated ML algorithms over several months (maybe years). First and foremost, we envisage the possibility of vast repositories of models (not merely datasets, solutions, or code). We believe the novelty of conservation machine learning, herein applied to random forests, is two-fold. There also exists a body of knowledge regarding ensemble pruning 6. Pooling algorithms, such as stacked generalization 4, and super learner 5, have also proven successful. They further examined three alternatives to their selection procedure to reduce overfitting. They used a simple hill-climbing procedure to build the final ensemble, and successfully tested their method on 7 problems. Reference 3 presented a method for constructing ensembles from libraries of thousands of models. It uses majority voting (for classification problems) or averaging (for regression problems) to improve predictive accuracy and control over-fitting 2. Conservation ML is essentially an “add-on” meta-algorithm, which can be applied to any collection of models (or even sub-models), however they were obtained: via ensemble or non-ensemble methods, collected over multiple runs, gathered from different modelers, a priori intended to be used in conjunction with others-or simply plucked a posteriori, and so forth.Ī random forest (RF) is an oft-used ensemble technique that employs a forest of decision-tree classifiers on various sub-samples of the dataset, with random subsets of the features for node splits. We recently presented the idea of conservation machine learning, wherein machine learning (ML) models are saved across multiple runs, users, and experiments 1.
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