MACHINE LEARNING

Detailed explanation-1: -In supervised learning, overfitting happens when our model captures the noise along with the underlying pattern in data. It happens when we train our model a lot over noisy dataset. These models have low bias and high variance. These models are very complex like Decision trees which are prone to overfitting.

Detailed explanation-2: -Specifically, overfitting occurs if the model or algorithm shows low bias but high variance. Overfitting is often a result of an excessively complicated model, and it can be prevented by fitting multiple models and using validation or cross-validation to compare their predictive accuracies on test data.

Detailed explanation-3: -If the student gets a 95% in the mock exam but a 50% in the real exam, we can call it overfitting. A low error rate in training data implies Low Bias whereas a high error rate in testing data implies a High Variance, therefore. In simple terms, Low Bias and Hight Variance implies overfittting.

Detailed explanation-4: -Relation With Overfitting And Underfitting A model with low variance and low bias is the ideal model (grade 1 model). A model with low bias and high variance is a model with overfitting (grade 9 model). A model with high bias and low variance is usually an underfitting model (grade 0 model).