How does Overfitting occur?
When a machine learning model learns all the intricacies and noise in the training data to the point where it adversely affects the model’s performance on new data, this is known as overfitting. The reason for this is that the model is very complicated in comparison to the volume and noise level of the training data. Poor generalization can result from overfitting, which happens when a model is too adaptive and fits the training data exactly—including noise. In addition, the model may be able to memorize the training data rather than understand the underlying patterns if it has too many parameters in comparison to the number of observations in the training set.
How can Feature Selection reduce overfitting?
The development of precise models is essential for predicted performance in the rapidly developing area of machine learning. The possibility of overfitting, in which a model picks up noise and oscillations unique to the training set in addition to the underlying patterns in the data, presents an inherent problem. As a potent weapon against overfitting, feature selection offers a way to improve the efficiency and generality of the model.