A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
| Autor: | Gjergji Kasneci, Klaus Broelemann |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Artificial neural network Computer science media_common.quotation_subject Machine Learning (stat.ML) 02 engineering and technology Ensemble learning Machine Learning (cs.LG) Statistics - Machine Learning Simple (abstract algebra) Gradient based algorithm 020204 information systems Transparency (graphic) 0202 electrical engineering electronic engineering information engineering Predictive power 020201 artificial intelligence & image processing Simplicity Algorithm media_common |
| Zdroj: | IJCAI |
| DOI: | 10.48550/arxiv.1809.09703 |
| Popis: | Machine learning algorithms aim at minimizing the number of false decisions and increasing the accuracy of predictions. However, the high predictive power of advanced algorithms comes at the costs of transparency. State-of-the-art methods, such as neural networks and ensemble methods, result in highly complex models with little transparency. We propose shallow model trees as a way to combine simple and highly transparent predictive models for higher predictive power without losing the transparency of the original models. We present a novel split criterion for model trees that allows for significantly higher predictive power than state-of-the-art model trees while maintaining the same level of simplicity. This novel approach finds split points which allow the underlying simple models to make better predictions on the corresponding data. In addition, we introduce multiple mechanisms to increase the transparency of the resulting trees. |
| Databáze: | OpenAIRE |
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