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What is scikit-learn?

less than 1 minute read

Published: September 11, 2012

Scikit-learn has been the lingua-franca of machine learning community for over a decade now. Developed in early 2000’s, the paper about scikit-learn came in 2011 and 2013. The paper is well written, and describes the basic principles of the library. The very fact that the paper of a machine learning library is still very much relevant today, speaks volumes about the strong principles and robust software design of scikit-learn library. No doubt, the library has influenced many onward machine learning libraries and almost all the ‘mainstream’ machine learning libraries have borrowed many concepts from it. Since the success of scikit-learn, there has been plethora of scikits, sickit-optimize for optimization of hyperparameters, scikit-image for image processing, to name a few. This post has been influenced by the original paper and my experience of using scikit-learn. The purpose is to provide an overview of scikit-learn with code examples.

publications

Surface and sub-surface flow estimation at high temporal resolution using deep neural networks

Published in Journal of Hydrology, 2020

Recommended citation: Abbas, A., Baek, S., Kim, M., Ligaray, M., Ribolzi, O., Silvera, N., ... & Cho, K. H. (2020). Surface and sub-surface flow estimation at high temporal resolution using deep neural networks. Journal of Hydrology, 590, 125370. https://doi.org/10.1016/j.jhydrol.2020.125370

Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models

Published in Water Research, 2021

This paper is prediction of antibiotic resistance genes at a recreational beach in South Korea using deep learning..

Recommended citation: Jeong, K., Abbas, A., Shin, J., Son, M., Kim, Y. M., & Cho, K. H. (2021). Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models. Water Research, 205, 117697. https://doi.org/10.1016/j.watres.2021.117697

Developing a deep learning model for the simulation of micro-pollutants in a watershed

Published in Journal of Cleaner Production, 2021

Recommended citation: Yun, D., Abbas, A., Jeon, J., Ligaray, M., Baek, S. S., & Cho, K. H. (2021). Developing a deep learning model for the simulation of micro-pollutants in a watershed. Journal of Cleaner Production, 300, 126858. https://doi.org/10.1016/j.jclepro.2021.126858

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

Published in Water Research, 2021

Recommended citation: Jang, J., Abbas, A., Kim, M., Shin, J., Kim, Y. M., & Cho, K. H. (2021). Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models. Water Research, 196, 117001. https://doi.org/10.1016/j.watres.2021.117001

Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant

Published in Desalination, 2021

Recommended citation: Yoon, N., Kim, J., Lim, J. L., Abbas, A., Jeong, K., & Cho, K. H. (2021). Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant. Desalination, 512, 115107. https://doi.org/10.1016/j.desal.2021.115107

In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models

Published in Hydrology and Earth System Sciences, 2021

Recommended citation: Abbas, A., Baek, S., Silvera, N., Soulileuth, B., Pachepsky, Y., Ribolzi, O., ... & Cho, K. H. (2021). In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models. Hydrology and Earth System Sciences, 25(12), 6185-6202. https://doi.org/10.5194/hess-25-6185-2021

Deep learning for pH prediction in water desalination using membrane capacitive deionization

Published in Desalination, 2021

Recommended citation: Son, M., Yoon, N., Jeong, K., Abass, A., Logan, B. E., & Cho, K. H. (2021). Deep learning for pH prediction in water desalination using membrane capacitive deionization. Desalination, 516, 115233. https://doi.org/10.1016/j.desal.2021.115233

Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

Published in Environmental Engineering Research, 2022

Recommended citation: Kwon, D. H., Hong, S. M., Abbas, A., Pyo, J., Lee, H. K., Baek, S. S., & Cho, K. H. (2023). Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environmental Engineering Research, 28(1). https://doi.org/10.4491/eer.2021.280

AI4Water v1. 0: An open-source python package for modeling hydrological time series using data-driven methods

Published in Geoscientific Model Development, 2022

Recommended citation: Abbas, A., Boithias, L., Pachepsky, Y., Kim, K., Chun, J. A., & Cho, K. H. (2022). AI4Water v1. 0: an open-source python package for modeling hydrological time series using data-driven methods. Geoscientific Model Development, 15(7), 3021-3039. https://doi.org/10.5194/gmd-15-3021-2022

Machine learning approaches to predict the photocatalytic performance of bismuch ferrite-based materials in the removal of malachite green

Published in Journal of Hazardous Materials, 2022

Recommended citation: Jaffari, Z. H., Abbas, A., Lam, S-M., Sanghun, P., Chon, K., Kim, E-S., & Cho, K. H. (2022). Machine learning approaches to predict the photocatalytic performance of bismuch ferrite-based materials in the removal of malachite green. Journal of Hazardous Materials, https://doi.org/10.1016/j.jhazmat.2022.130031

An open-source deep learning model for predicting effluent concentration in capacitive deionization

Published in Science of the Total Environment, 2023

Recommended citation: Son, M., Yoon, N., Park, S., Abbas, A., & Cho, K. H. (2023). An open-source deep learning model for predicting effluent concentration in capacitive deionization. Science of The Total Environment, 856, 159158. https://doi.org/10.1016/j.scitotenv.2022.159158

Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions

Published in Journal of Materials Chemistry A, 2023

Recommended citation: Jaffari, Z. H., Abbas, A., Umar, M., Kim, E-S., & Cho, K. H. (2023). Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(II) and Cd(II) ions https://doi.org/10.1039/D3TA00019B

Developing a data-driven modeling framework for simulating a chemical accident in freshwater

Published in Journal of Cleaner Production, 2023

Recommended citation: Kim, S., Abbas, A., Pyo, J., Kim, H., Hong, S. M., Baek, S. S., & Cho, K. H. (2023). Developing a data-driven modeling framework for simulating a chemical accident in freshwater. Journal of Cleaner Production, 425, 138842. https://doi.org/10.1016/j.jclepro.2023.138842

Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials

Published in Separation and Purification Technology, 2023

Recommended citation: Iftikhar, S., Zahra, N., Rubab, F., Sumra, R. A., Khan, M. B., Abbas, A., & Jaffari, Z. H. (2023). Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials. Separation and Purification Technology, 326, 124891. https://doi.org/10.1016/j.seppur.2023.124891

Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms

Published in Ecological Informatics, 2023

Recommended citation: Jang, J., Abbas, A., Kim, H., Rhee, C., Shin, S. G., Chun, J. A., ... & Cho, K. H. (2023). Prediction and interpretation of pathogenic bacteria occurrence at a recreational beach using data-driven algorithms. Ecological Informatics, 102370. https://authors.elsevier.com/c/1i5qV5c6cL2WXM

Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Published in Environmental Modeling and Software, 2023

Deep learning-based super-resolution for harmful algal bloom monitoring of inland water

Published in GIS and Remote Sensing, 2023

Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents

Published in Journal of Hazardous materials, 2023

Recommended citation: https://doi.org/10.1016/j.jhazmat.2023.132773

Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams

Published in Journal of Hydrology, 2023

Recommended citation: Abbas, A., Park, M., Baek, S. S., & Cho, K. H. (2023). Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams. Journal of Hydrology, 626, 130240. https://doi.org/10.1016/j.jhydrol.2023.130240

Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal

Published in Journal of Hazardous Materials, 2023

Recommended citation: Kim, C. M., Jaffari, Z. H., Abbas, A., Chowdhury, M. F., & Cho, K. H. (2023). Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal. Journal of Hazardous Materials, 132995. https://doi.org/10.1016/j.jhazmat.2023.132995

Long Short-Term Memory models of water quality in inland water environments

Published in Water Research X, 2023

Ather Abbas

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