Titlebook: Artificial Intelligence and Machine Learning; 31st Benelux AI Conf Conference proceedings 2020

親密

cleaver

Tailor

Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inferenceones. Though retention campaigns may be used to prevent customer churn, their success depends on the availability of accurate prediction models. Churn prediction is notoriously a difficult problem because of the large amount of data, non-linearity, imbalance and low separability between the classes

警告

vanquish

癡呆

Cognitively Plausible Computational Models of Lexical Processing Can Explain Variance in Human Word ng times from the PROVO corpus (Luke and Christianson .). A recurrent neural network is able to explain variance in human prediction errors whereas the Rescorla-Wagner model performs less well. The Rescorla-Wagner prediction associations do however explain more variance in human reading times. Moreo

干旱

無聊點好

Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inferenceing. Results show that feature selection can be used to reduce computation time and memory requirements, though engineering variables does not necessarily improve performance. In the second part of the paper we explore the application of data-driven causal inference, which aims to infer causal relat

Consequence

Dividing the Light from the Darknessn bounded and unbounded workspaces and we compare the results with those of AutoMoDe-Chocolate in order to understand the impact of the new exploration schemes. The results show that Coconut is prone to select exploration schemes that fulfill the requirements of the mission in hand. However, Coconut

PON