A (Very) Brief Explanation of Curve-Fitting Method

Source: CIMS

According to Wikipedia, Curve fitting is the process of constructing a curve or mathematical function that best fits a series of data points, possibly subject to constraints.

Basically, it’s a regression analysis to find a function that closely approximates the relationship between the variables (independent and dependent).

There are various methods of curve fitting, including but not limited to the following:

1. Linear

   The linear curve method fits a straight line to the data points and models linear relationships between two variables.

2. Polynomial

   This method fits a polynomial function to the data points and is used to model non-linear relationships between two variables.

3. Exponential

   The exponential method fits the exponential function to the data points and is used to model relationships where the dependent variable increases/decreases exponentially with the independent variable.

4. Logarithmic

   The logarithmic method fits the logarithmic function to the data points. It is used to model relationships where the dependent variable changes at a decreasing rate as the independent variable increases.

5. Power

   This method fits a power function to the data points and is used to model relationships where the dependent variable changes proportionally with the independent variable raised to a power.

The following image explains the mathematical function for the above list.

Source: statisticsfromatoz

That is all for today! Please comment if you want to know something else from Machine Learning and Python domain!