Penn Machine Learning Benchmark
2 papers with code • 1 benchmarks • 0 datasets
Penn Machine Learning Benchmarks (PMLB) is a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms.
Benchmarks
These leaderboards are used to track progress in Penn Machine Learning Benchmark
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Most implemented papers
An Evolutionary Forest for Regression
hengzhe-zhang/EvolutionaryForest
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IEEE Transactions on Evolutionary Computation 2021
Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years.
SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
hengzhe-zhang/EvolutionaryForest
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IEEE Transactions on Evolutionary Computation 2023
Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance.
