What is overfitting and underfitting in machine learning? What is Bias and Variance? Overfitting and Underfitting are two common problems in machine learning and Deep learning. If a model has low ...

Overfitting in ML is when a model learns training data too well, failing on new data. Investors should avoid overfitting as it mirrors risks of betting on past stock performances. Techniques like ...

Nate SIlver's "The Signal and the Noise" (recommended, buy it, read it, etc.) includes a great discussion of the problem of "overfitting": When you see apparent relationships in data that aren't ...

Ernie Smith is a former contributor to BizTech, an old-school blogger who specializes in side projects, and a tech history nut who researches vintage operating systems for fun. In data analysis, it is ...

Providence, RI---Your financial advisor calls you up to suggest a new investment scheme. Drawing on 20 years of data, he has set his computer to work on this question: If you had invested according to ...

Our data science expert continues his exploration of neural network programming, explaining how regularization addresses the problem of model overfitting, caused by network overtraining. Neural ...