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Preprocessor Selection for Machine Learning Pipelines
Much of the work in metalearning has focused on classifier selection, combined more recently with hyperparameter optimization, with little concern for data preprocessing.
Recommending Learning Algorithms and Their Associated Hyperparameters
The success of machine learning on a given task dependson, among other things, which learning algorithm is selected and its associated hyperparameters.
An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage
The results from most machine learning experiments are used for a specific purpose and then discarded.
The Potential Benefits of Filtering Versus Hyper-Parameter Optimization
We find that, while both significantly improve the induced model, improving the quality of the training set has a greater potential effect than hyper-parameter optimization.
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