Metrics for benchmarking and uncertainty quantification: Quality, applicability, and best practices for machine learning in chemistry
This review aims to draw attention to two issues of concern when we set out to make
machine learning work in the chemical and materials domain, that is, statistical loss function …
machine learning work in the chemical and materials domain, that is, statistical loss function …
A survey of cost-sensitive decision tree induction algorithms
S Lomax, S Vadera - ACM Computing Surveys (CSUR), 2013 - dl.acm.org
The past decade has seen a significant interest on the problem of inducing decision trees
that take account of costs of misclassification and costs of acquiring the features used for …
that take account of costs of misclassification and costs of acquiring the features used for …
[HTML][HTML] Creating and detecting fake reviews of online products
Customers increasingly rely on reviews for product information. However, the usefulness of
online reviews is impeded by fake reviews that give an untruthful picture of product quality …
online reviews is impeded by fake reviews that give an untruthful picture of product quality …
[图书][B] Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных
П Флах - 2022 - books.google.com
Перед вами один из самых интересных учебников по машинному обучению–разделу
искусственного интеллекта, изучающего методы построения моделей, способных …
искусственного интеллекта, изучающего методы построения моделей, способных …
[图书][B] Machine learning: the art and science of algorithms that make sense of data
P Flach - 2012 - books.google.com
As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …
Generalized and scalable optimal sparse decision trees
Decision tree optimization is notoriously difficult from a computational perspective but
essential for the field of interpretable machine learning. Despite efforts over the past 40 …
essential for the field of interpretable machine learning. Despite efforts over the past 40 …
Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods
VF Rodriguez-Galiano, JA Luque-Espinar… - Science of the total …, 2018 - Elsevier
Recognising the various sources of nitrate pollution and understanding system dynamics
are fundamental to tackle groundwater quality problems. A comprehensive GIS database of …
are fundamental to tackle groundwater quality problems. A comprehensive GIS database of …
[图书][B] Data mining with decision trees: theory and applications
Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …
knowledge discovery and data mining; it is the science of exploring large and complex …
The relationship between Precision-Recall and ROC curves
J Davis, M Goadrich - Proceedings of the 23rd international conference …, 2006 - dl.acm.org
Receiver Operator Characteristic (ROC) curves are commonly used to present results for
binary decision problems in machine learning. However, when dealing with highly skewed …
binary decision problems in machine learning. However, when dealing with highly skewed …
A study of the behavior of several methods for balancing machine learning training data
There are several aspects that might influence the performance achieved by existing
learning systems. It has been reported that one of these aspects is related to class …
learning systems. It has been reported that one of these aspects is related to class …