Machine Learning: The Ultimate Beginners Guide For Neural Networks, Algorithms, Random Forests and Decision Trees Made Simple by Ryan Roberts

Author:Ryan Roberts
Language: eng
Format: azw3, epub
Published: 2017-07-28T07:00:00+00:00

The reason this is known as a linear regression is because the data points can be plotted on a single line, represented by a linear equation of Y=aX+b. In this equation, “Y” is the dependent variable (or solution), “X” is the independent variable, “a” is the slope of the regression line, and “b” is the intercept point of the line with the axis.

People use a form of linear regression every day to make educated assumptions about the world around them. Say you were to try and order a group of people according to their weight, but didn’t have a scale on hand. You would probably look at other factors, like their height, build, and gender, and use this to estimate how they would be ranked by using a combination of these visible parameters.

Linear regression basically does the same thing. It uses typical correlations and data that’s given to generate new data. If it has been well-trained, it will be able to return incredibly accurate results, successfully predicting a wide range of different factors.

There are two main types of this style. Simple linear regression has only a single independent variable, meaning it can be modeled in two-dimensional space. Multiple linear regression, on the other hand, has more than one independent variable. The modeling for this method can be a bit more complex; you may need to look toward polynomial or curvilinear best fit lines.

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