Machine Learning: An In-Depth Guide - Model Evaluation, Validation, Complexity, and Improvement

Welcome to the third article in a five-part series about machine learning. In this article, we'll continue our machine learning discussion, and focus on problems associated with overfitting data, as well as controlling model complexity, a model evaluation and errors introduction, model validation and tuning, and improving model performance. Overfitting is one of the greatest concerns in predictive analytics and machine learning. Overfitting refers to a situation where the model chosen to fit the training data fits too well, and essentially captures all of the noise, outliers, and so on. The consequence of this is that the model will fit the training data very well, but will not accurately predict cases not represented by the training data, and therefore will not generalize well to unseen data.