Last Updated on July 18, 2023 by Editorial Team
Author(s): Kaushik Choudhury
Originally published on Towards AI.
Select appropriate classifiers empirically and automatically for the prediction scenarios from scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.
Photo by Clay Banks on Unsplash
As machine learning professionals, we must consider several aspects to develop a good model. It involves exploratory data analysis, data cleansing, selecting the optimal set of independent variables, picking the most appropriate algorithm, implementing it efficiently, fine-tuning the parameters to predict the outcome more accurately, and a long list of other elements.
In this long sequence of activities, one of the time-consuming and complex tasks is identifying the most appropriate… Read the full blog for free on Medium.
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Published via Towards AI