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 ...

This video is an overall package to understand Dropout in Neural Network and then implement it in Python from scratch. Dropout in Neural Network is a regularization technique in Deep Learning to ...

A condition whereby an AI model is not generalized sufficiently for all uses. Although it does well on the training data, overfitting causes the model to perform poorly on new data. Overfitting can ...

The Econometrics Journal, Vol. 21, No. 1, RES CONFERENCE 2016: SPECIAL ISSUE ON MODEL SELECTION AND INFERENCE (2018), pp. C1-C68 (68 pages) We revisit the classic semi-parametric problem of inference ...