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.
5 Big Data challenges can be highlighted which are defined as V (volume, velocity, veracity, variety and value). R. Narasimhan discussed 3V with [...]
Read More »How is artificial intelligence helping us? Artificial intelligence (AI) has gone from being the stuff of science fiction movies to a [...]
Read More »Before explaining what artificial intelligence is, we would like to start with a sentence from the book Age of intelligent machines (1992), by Raymond Ku [...]
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