There are two types of models in machine learning: parametric and non-parametric. The predictor variables that are utilized to create the machine learning model are referred to as parameters. The following is an explanation of these models:
Parametric Model:
The ML model is created using a set number of parameters in parametric models. It takes into account a number of strong data assumptions. Linear regression, Logistic Regression, Naive Bayes, Perceptron, and other parametric models are examples.
Non-Parametric Model:
The non-parametric model has a large number of parameters that may be changed. It takes into account a few data assumptions. These models work well with large amounts of data and no prior information. Decision Tree, K-Nearest Neighbour, SVM with Gaussian kernels, and other non-parametric models are examples.