fit data using curves, surfaces, and nonparametric methods
data fitting is the process of fitting models to data and analyzing the accuracy of the fit. engineers and scientists use data fitting techniques, including mathematical equations and nonparametric methods, to model acquired data.
matlab® lets you import and visualize your data, and perform basic fitting techniques such as polynomial and spline interpolation. you can perform data fitting interactively using the , or programmatically using .
matlab add-on products extend data fitting capabilities to:
- fit curves and surfaces to data using the functions and app in curve fitting toolbox™. several linear, nonlinear, parametric, and nonparametric models are included. you can also define your own custom models.
- fit n-dimensional data using the linear and nonlinear regression capabilities in statistics and machine learning toolbox™. you can also use machine learning algorithms for data-driven fitting.
- perform constrained data fitting where parameters need to satisfy linear or nonlinear constraints with optimization toolbox™.
getting started
examples
software reference
see also: data analysis, mathematical modeling, smoothing, machine learning, , automl