[{"data":1,"prerenderedAt":12},["ShallowReactive",2],{"stack-tr-en-scikit-learn":3},{"tagline":4,"description":5,"strengths":6,"useCase":11},"The reference Python toolkit for classical machine learning.","scikit-learn covers classification, regression, clustering, dimensionality reduction, model selection and preprocessing, with a consistent fit\u002Fpredict API across every algorithm. Built on NumPy and SciPy, it's the right choice for tabular data when deep learning is overkill and you want reliable models that are fast to train and simple to ship to production.",[7,8,9,10],"Uniform fit\u002Fpredict\u002Ftransform API across dozens of algorithms.","Pipelines and cross-validation for a reproducible ML workflow.","Model selection: grid\u002Frandom search, metrics, calibration.","Open source (BSD), outstanding docs, NumPy\u002FSciPy ecosystem.","Scoring, customer segmentation and prediction on tabular data.",1792675974716]