The first predictive model built in any project is typically a regression. The mother of all “white-box models,” a linear regression provides a straightforward implementation and interpretation. It’s clear for us to see which variables have a large influence on our target and we can also use statistical tests to check the significance of these coefficients. Following inspiration from Occam’s Razor, that “entities should not be multiplied beyond necessity”[1], it makes logical sense to build up in complexity in our models.
The 3 Key Variations of Linear Regression
The 3 Key Variations of Linear Regression
The 3 Key Variations of Linear Regression
The first predictive model built in any project is typically a regression. The mother of all “white-box models,” a linear regression provides a straightforward implementation and interpretation. It’s clear for us to see which variables have a large influence on our target and we can also use statistical tests to check the significance of these coefficients. Following inspiration from Occam’s Razor, that “entities should not be multiplied beyond necessity”[1], it makes logical sense to build up in complexity in our models.