Overfitting is a term used in machine learning to describe a model that has been overfitted to the training data, resulting in poor performance on new or unseen data. That is, the model has learned the training data "by heart", rather than capturing the underlying relationships in the data. This can occur when the model is too complex or is trained for too long, leading to an increased ability of the model to fit the training data rather than generalising to new data. Methods to avoid over-fitting include cross-validation, reducing model complexity and adding regularisation.
Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied.
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