Module 1: Classification Models

Classification Models - Lesson Summary

Bivariate analysis refers to the simultaneous analysis of two variables. It explores the possibility of a relationship or differences between two variables and the resulting significance.
Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. A positive correlation indicates the extent to which those variables increase or decrease in parallel, whilst a negative correlation indicates the extent to which one variable increases as the other one decreases.
Seasonality refers to the presence of variations in data that occurs at specific regular intervals that are less than a year.
The Residual Standard Error (RSE) is the average amount that the response will deviate from the true regression line and it can also be considered as a measure of the absence of fit between the model and data.
Dummy coding is a way of incorporating nominal variables into regression analysis.
The Linear Discriminant Analysis classification model is best used for multi-class response variables.
The K-Nearest Neighbors classification technique does not assume any functional form of the relationship between variables.