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.
Just like in life, one size of clothing doesn’t fit every one of us, in machine learning, one classifier doesn’t perform well for different situations and datasets.
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