El popular dicho español ‘El que mucho abarca poco aprieta’ ha cobrado gran relevancia en la era del análisis de datos. Cuando los data scientists, data analysts, y analistas en general realizan modelos para predecir comportamientos, tendencias, patrones, etc. se enfrentan ante el desafío de abarcar lo suficiente para apretar
This is the seventh post in my series of machine best practices. Catch up by reading the first post or the whole series now. Generalization is the learned model’s ability to fit well to new, unseen data instead of the data it was trained on. Overfitting refers to a model that fits
When building models, data scientists and statisticians often talk about penalty, regularization and shrinkage. What do these terms mean and why are they important? According to Wikipedia, regularization "refers to a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information usually
