What Is ANOVA in Machine Learning?
Predictive analysis and machine learning have become crucial parts of the digital transformation, regardless of whatever sector a business dives into. It’s important to have the ability to assess the main and interaction effects among different variables. This helps to create a more streamlined decision-making process, backed up by evidence within the statistics perhaps not seen before. One way in which this is accomplished is through the analysis of variance, or ANOVA.
What is ANOVA?
Analysis of variance, or ANOVA, is a statistical formula used to compare variances across the averages of different groups. A range of scenarios use it to determine if there is any difference between the means of different groups. For example, a pharmaceutical company may use this formula to determine the effectiveness of medications. A group of patients will be among the test subjects for a variety of dosages to measure the impact of the distribution of a particular drug to, say, diabetes patients across the U.S.
The outcome of ANOVA is the ‘F statistic.’ This ratio shows the difference between the within-group variance and the between-group variance, which ultimately produces a figure which allows a conclusion that the null hypothesis is supported or rejected. If there is a significant difference between the groups, the null hypothesis is not supported, and the F-ratio will be larger. By evaluating these hypotheses with a statistical formula, there’s a better assessment of the effect size that is linked to these test statistics to find any significant results.
ANOVA Terminology
Analysis of variance is broken down into two types: one-way and two-way ANOVA. One-way ANOVA, or single-factor ANOVA, is suitable for experiments in machine learning with just one independent variable and a particular dependent variable. This could be in determining the output of a supply chain for seasonal equipment. Two-way, or full factorial ANOVA, is used when there are two or more independent variables. This could be through multiple possible permutations to monitor for any replications or redundancies that could skew from the main effect of a business process. This can return to degrees of freedom within these decisions.
Independent variables are the items being measured that may have an effect on the dependent variable, which is the item being measured that is theorized to be affected by the independent variable, also known as a factor. Fixed-factor models offer some experiments using a discrete set of levels for factors. This can be used by manufacturers to get an understanding of statistical significance in the machine learning process. Random-factor models draw from a random value, denoting different states of the independent variable to gain the easiest understanding of a sample mean in a greater sample size.
ANOVA and Machine Learning
ANOVA helps in selecting the best features to minimize the number of input variables to reduce the complexity of a single-factor or fixed-factor model. ANOVA helps to determine if an independent variable is influencing a target variable to garner a significant result not previously anticipated. As a rule of thumb, this formula should be conducted on larger sample sizes for the proper assessment of a mean square. This finds any trends of replication that can prevent issues down the line.
One of the common uses of analysis of variance may be through email spam detection. Since accounts may be dealing with a massive number of emails and email features on a given day, ANOVA correctly identifies which emails were spam and which were not. This correction saves account holders time in their inbox which, in circumstances, allots time for more important decisions. By having the proper assessment of a treatment group, ANOVA can help to make the best decisions going forward.
