Nonlinear regression with python gekko

nonlinear regression is a type of regression analysis in which the relationship between the independent variables and the dependent variable is modeled as a nonlinear function. it is commonly used when the relationship between the variables cannot be adequately described by a linear model. python offers various libraries for nonlinear regression, and one popular choice is gekko.

gekko is a python library for optimization and machine learning that can be used for nonlinear regression. it provides a high-level interface for solving optimization problems, including regression tasks. in this tutorial, we will demonstrate how to perform nonlinear regression using gekko.

first, you need to install gekko using pip:

now, let's create an example of performing nonlinear regression using gekko. in this example, we will fit a nonlinear function to some synthetic data.

in this code example, we first generate some synthetic data using a nonlinear function. then, we initialize a gekko model and define the coefficients `a` and `b` that we want to optimize. we define the nonlinear model as `a * sin(b * x)` and set up an objective function to minimize the sum of squared errors between the predicted values and the actual data.

we then set the optimization mode and solve the model to get the optimized coefficients `a_opt` and `b_opt`. finally, we plot the original data points and the predicted curve using the optimized coefficients.

this is a basic example of performing nonlinear regression using gekko in python. you can customize the model and objective function based on your specific regression task and data.

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#python gekko examples
#python gekko pid
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#python gekko minlp
#python gekko install

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