Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm’s predictive performance, and—if possible—derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass–classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison.
Modern machine learning methods highly depend on their hyperparameter configurations for optimal performance. A widely used approach to selecting a configuration is using default settings, often proposed along with the publication of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work on a wide variety of datasets. Different automatic hyperparameter configuration algorithms which select an optimal configuration per dataset have been proposed, but despite its importance, tuning is often skipped in applications because of additional run time, complexity, and experimental design questions. Instead, the learner is often applied in its defaults. This principled approach usually improves performance but adds additional algorithmic complexity and computational costs to the training procedure. We propose and study using a set of complementary default values, learned from a large database of prior empirical results as an alternative. Selecting an appropriate configuration on a new dataset then requires only a simple, efficient, and embarrassingly parallel search over this set. To demonstrate the effectiveness and efficiency of the approach, we compare learned sets of configurations to random search and Bayesian optimization. We show that sets of defaults can improve performance while being easy to deploy in comparison to more complex methods. CCS CONCEPTS• Computing methodologies → Supervised learning by classification.
A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research in this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with different distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results shows that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candiates. Local:D obRemote: Dcu... ...
When developing and analyzing new hyperparameter optimization (HPO) methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites. In this work, we list desirable properties and requirements for such benchmarks and propose a new set of challenging and relevant multifidelity HPO benchmark problems motivated by these requirements. For this, we revisit the concept of surrogate-based benchmarks and empirically compare them to more widely-used tabular benchmarks, showing that the latter ones may induce bias in performance estimation and ranking of HPO methods. We present a new surrogate-based benchmark suite for multifidelity HPO methods consisting of 9 benchmark collections that constitute over 700 multifidelity HPO problems in total. All our benchmarks also allow for querying of multiple optimization targets, enabling the benchmarking of multi-objective HPO. We examine and compare our benchmark suite with respect to the defined requirements and show that our benchmarks provide viable additions to existing suites. * Equal contribution Preprint. Under review.
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