5,715 research outputs found
Is One Hyperparameter Optimizer Enough?
Hyperparameter tuning is the black art of automatically finding a good
combination of control parameters for a data miner. While widely applied in
empirical Software Engineering, there has not been much discussion on which
hyperparameter tuner is best for software analytics. To address this gap in the
literature, this paper applied a range of hyperparameter optimizers (grid
search, random search, differential evolution, and Bayesian optimization) to
defect prediction problem. Surprisingly, no hyperparameter optimizer was
observed to be `best' and, for one of the two evaluation measures studied here
(F-measure), hyperparameter optimization, in 50\% cases, was no better than
using default configurations.
We conclude that hyperparameter optimization is more nuanced than previously
believed. While such optimization can certainly lead to large improvements in
the performance of classifiers used in software analytics, it remains to be
seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service
In this paper, we present machine learning approaches for characterizing and
forecasting the short-term demand for on-demand ride-hailing services. We
propose the spatio-temporal estimation of the demand that is a function of
variable effects related to traffic, pricing and weather conditions. With
respect to the methodology, a single decision tree, bootstrap-aggregated
(bagged) decision trees, random forest, boosted decision trees, and artificial
neural network for regression have been adapted and systematically compared
using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and
slope. To better assess the quality of the models, they have been tested on a
real case study using the data of DiDi Chuxing, the main on-demand ride hailing
service provider in China. In the current study, 199,584 time-slots describing
the spatio-temporal ride-hailing demand has been extracted with an
aggregated-time interval of 10 mins. All the methods are trained and validated
on the basis of two independent samples from this dataset. The results revealed
that boosted decision trees provide the best prediction accuracy (RMSE=16.41),
while avoiding the risk of over-fitting, followed by artificial neural network
(20.09), random forest (23.50), bagged decision trees (24.29) and single
decision tree (33.55).Comment: Currently under review for journal publicatio
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box
operations not accessible through traditional techniques. The success of
Bayesian optimization methods such as Expected Improvement (EI) are
significantly affected by the degree of trade-off between exploration and
exploitation. Too much exploration can lead to inefficient optimization
protocols, whilst too much exploitation leaves the protocol open to strong
initial biases, and a high chance of getting stuck in a local minimum.
Typically, a constant margin is used to control this trade-off, which results
in yet another hyper-parameter to be optimized. We propose contextual
improvement as a simple, yet effective heuristic to counter this - achieving a
one-shot optimization strategy. Our proposed heuristic can be swiftly
calculated and improves both the speed and robustness of discovery of optimal
solutions. We demonstrate its effectiveness on both synthetic and real world
problems and explore the unaccounted for uncertainty in the pre-determination
of search hyperparameters controlling explore-exploit trade-off.Comment: Accepted for publication in the proceedings of 2018 Computing
Conferenc
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