An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage

Abstract. The results from most machine learning experiments are used for a specific purpose and then discarded. This causes sig-nificant loss of information and requires rerunning experiments to compare learning algorithms. Often, this also requires a researcher or practitioner to implement another algorithm for comparison, that may not always be correctly implemented. By storing the results from previous experiments, machine learning algorithms can be compared easily and the knowledge gained from them can be used to improve the performance of future machine learning experiments. The purpose of this work is to provide easy access to previous ex-perimental results for learning and comparison. These stored results are comprehensive – storing the prediction for each test instance as well as the learning algorithm, hyperparameters, and training set that were used in the experiment. Previous experimental results are par-ticularly important for meta-learning, which, in a broad sense, is the process of learning from previous machine learning results such that the learning process is improved. While other experiment databases do exist, one of our focuses is on easy access to the data, eliminat-ing any learning curve required to acquire the desired information. We provide meta-learning data sets that are ready to be downloaded for meta-learning experiments. Easy access to previous experimental results aids other researchers looking to do meta-learning and helps in comparing meta-learning algorithms. In addition, simple queries to the underlying database can be made if specific information is de-sired. We also differ from previous experiment databases in that our database is designed at the instance level, where an instance is an ex-ample in a data set. We store the predictions of a learning algorithm trained on a specific training set for each instance in the test set. Data set level information can then be obtained by aggregating the results from the instances. The instance level information can be used for many tasks such as determining the diversity of a classifier or algo-rithmically determining the optimal subset of training instances for a learning algorithm.