linear regression equation example equation

Intercept - It is the point at where the slope intersects the Y-axis, indicated by the letter b in the slope equation y=ax+b.

Also referred to as an Input or a predictor

Independent Feature - a variable represented by the letter x in the slope equation y=ax+b.

Estimated Regression Line - the straight line that best fits a set of randomly distributed data points.

Also referred to as an Output or a Response

Dependent Feature - A variable represented as y in the slope equation y=ax+b.

Correlation - the measurable intensity and degree of association between two variables, often known as the 'degree of correlation.' The values range from -1.0 to 1.0.

In a regression, this term is used to define the precision or degree of fit

Coefficient of Determination - It is the correlation coefficient.

A coefficient in linear regression represents changes in a Response Variable

Coefficient - Also known as a parameter, is the factor that is multiplied by a variable.

Best Fit - The straight line in a plot that minimizes the divergence between related dispersed data points.

When working with scikit-linear learn's regression approach, you will encounter the following fundamental concepts: Because of its simplicity and essential features, linear regression is a fundamental Machine Learning method. The line can then be projected to forecast fresh data points. Linear regression is defined as the process of determining the straight line that best fits a set of dispersed data points: Scikit-learn is a Python package that makes it easier to apply a variety of Machine Learning (ML) algorithms for predictive data analysis, such as linear regression.

Linear regression equation example equation