R-universe search: topic:stacking

Showing 6 of total 6 results (show query)

mlr-org

mlr3pipelines:Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

Maintained by Martin Binder. Last updated 26 days ago.

bagging data-science dataflow-programming ensemble-learning machine-learning mlr3 pipelines preprocessing stacking

141 stars 12.36 score 448 scripts 7 dependents

tlverse

sl3:Pipelines for Machine Learning and Super Learning

A modern implementation of the Super Learner prediction algorithm, coupled with a general purpose framework for composing arbitrary pipelines for machine learning tasks.

Maintained by Jeremy Coyle. Last updated 5 months ago.

data-science ensemble-learning ensemble-model machine-learning model-selection regression stacking statistics

100 stars 9.94 score 748 scripts 7 dependents

business-science

modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Maintained by Matt Dancho. Last updated 9 months ago.

ensemble ensemble-learning forecast forecasting modeltime stacking stacking-ensemble tidymodels time time-series timeseries

77 stars 8.30 score 143 scripts

vspinu

unnest:Unnest Hierarchical Data Structures

Fast flattening of hierarchical data structures (e.g. JSON, XML) into data.frames with a flexible spec language.

Maintained by Vitalie Spinu. Last updated 15 days ago.

data-frame flattened-data flattened-json flattening hierarchical-data stacking unnesting cpp

10 stars 5.00 score 5 scripts

haghish

autoEnsemble:Automated Stacked Ensemble Classifier for Severe Class Imbalance

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Maintained by E. F. Haghish. Last updated 9 days ago.

ai algorithm automated-machine-learning automl automl-algorithms ensemble ensemble-learning h2o h2oai machine-learning machinelearning metalearning stack-ensemble stacked-ensembles stacking

5 stars 4.42 score 21 scripts

epiforecasts

stackr:Create Mixture Models From Predictive Samples

The `stackr` package provides an easy way to combine predictions from individual time series or panel data models to an ensemble. `stackr` stacks (Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman (2018) <doi:10.1214/17-BA1091>) Models according to the Continuous Ranked Probability Score (CRPS) (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>) over k-step ahead predictions. It is therefore especially suited for timeseries and panel data. A function for leave-one-out CRPS may be added in the future. Predictions need to be predictive distributions represented by predictive samples. Usually, these will be sets of posterior predictive simulation draws generated by an MCMC algorithm. Given some training data with true observed values as well as predictive samples generated from different models, `crps_weights` finds the optimal (in the sense of minimizing expected cross-validation predictive error) weights to form an ensemble from these models. Using these weights, `mixture_from_samples` can then provide samples from the optimal model mixture by drawing from the predictice samples of the individual models in the correct proportion. This gives a mixture model solely based on predictive samples and is in this regard superior to other ensembling techniques like Bayesian Model Averaging.

Maintained by Nikos Bosse. Last updated 5 months ago.

crps ensembles forecasting stacking cpp

5 stars 4.34 score 44 scripts