What Does a Fitting Model Do?

Visually speaking, fitting is to connect a series of points on a plane with a smooth curve. Because there are countless possibilities for this curve, there are various fitting methods. The fitted curve can generally be represented by a function, and there are different fitting names depending on the function.

If the pending function is

R ^ 2 measures

In actual work, there is not necessarily a linear relationship between variables, such as the relationship between blood concentration and time after medication; the relationship between disease efficacy and duration of treatment; the relationship between the dose of poison and lethality is often a curve. Curve fitting refers to selecting the appropriate curve type to fit the observation data, and using the fitted

MATLAB Curve Fitting Toolbox

Fitting Introduction

MATLAB can do curve fitting through built-in functions or Curve Fitting Toolbox. This toolbox integrates graphical user interfaces (GUIs) and M-file functions built with MATLAB. Use this toolbox to perform parametric fitting (parametric fitting when you want to find the regression coefficients and the physical meaning behind them), or non-parametric fitting by using smoothing splines or other various interpolation methods ( (When the regression coefficients have no physical meaning and they do not care about them, use the non-parametric fitting method). Using this interface, you can quickly implement many basic curve fittings in an easy-to-use environment.

Improved fit results

Many factors will affect the curve fitting, resulting in good and bad fitting results. Here we only discuss from some perspectives that it is possible to improve the fitting quality.

1) Model selection: This is the most important factor. Try to fit and compare the data with various models;

2) Data preprocessing: It is also useful to preprocess the data before fitting. This includes transforming the response data and excluding Infs, NaNs, and points with obvious errors.

3) A reasonable fit should have the ability to handle singularities that make predictions approach infinity.

4) The more the estimated information of the coefficients is known, the easier it is for the fit to converge.

5) Decompose the data into several subsets, and use different curve fitting for different subsets.

6) Complex problems are best solved by evolution, that is, a small number of independent variables of a problem are solved first. Solutions for low-order problems usually use approximate mapping as the starting point for higher-order problems. ^[3]