Fitting or training in machine learning is the process by which a model learns from input data. Fitting is another word for training an ML model. Besides the ideal best fit or good fit, a model can get overfitted when overfitting occurs or it can get underfitted when underfitting occurs.
Overfitting in machine learning is a problem in which the ML model fails to generalize well to unseen data because its predictions very tightly much the training data. An overfitting model has high variance and low bias. Overfitting is the other pole to underfitting. The desired situation between overfitting and underfitting is a good fit ... Read more
Underfitting is a problem in machine learning in which a model cannot make effective target feature value estimations due to an inability to identify the underlying patterns in the data. An underfitting ML model has low variance and high bias. Underfitting is one pole away from a good fit, the other pole being overfitting.
Variance in machine learning is a measurement of the spread between numbers in a dataset (x independent variables) or a measurement of the variation of an ML model's estimations across different datasets. ML models with high bias and low variance lead to underfitting. ML models with low bias and high variance lead to overfitting. ML ... Read more
