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Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction RatesThis work is partly based on results obtained from a project, JPNP16010, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
The prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.
Key wordsmachine learning - catalytic reactions - reaction rates - ensemble learning - small datasets
Received: 31 July 2020
Accepted after revision: 05 November 2020
Accepted Manuscript online:
05 November 2020
Article published online:
07 January 2021
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References and Notes
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