——————————————————————— OVERFITTING and UNDER-FITTING ——————————————————————— The concept of overfitting refers to creating a model that doesn't generalize to your model. In other words, if your model overfits your data, that means it's learned your data too much - it's essentially memorized it. This might not seem like it would be a problem at first, but a model that's just "memorized" your data is one that's going to perform poorly on new, unobserved data. - Underfitting, on the other hand, is when your model is too generalized to your data. This model will also perform poorly on new unobserved data. This usually means we should increase the number of considered features, which will expand the hypothesis space. ———————————————————————— - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #simulation #science #mathconcepts #datascienceweekend #overfitting #underfitted #trainingset #testingset #validation #predictionerror (at United States)