discerning simplified latent structure from your data not everything can be measured when you meet someone for the first time you might try to guess whether they like you based on their behavior and how they respond but what if they've had bad day up until nowmaybe their cat got run over or they're still down from attending funeral the week beforethe point is that certain variables can be immediately available while others can only be inferred and are therefore missing from your data set the first type of variables are known as observable variables and the second type are known as latent variables in our examplethe emotional state of your new friend is latent variable it definitely influences their judgment of you but its value isn' clear deriving or inferring latent variables and their values based on the actual contents of data set is valuable skill to have because latent variables can substitute for several existing variables already in the data set by reducing the number of variables in the data setthe data set becomes more manageableany further algorithms run on it work fasterand predictions may become more accurate because latent variables are designed or targeted toward the defined research goalyou lose little key information by using them if we can reduce data set from observable variables per line to or latent variablesfor examplewe have better chance of reaching our research goal because of the data set' simplified structure as you'll see from the example belowit' not case of reducing the existing data set to as few latent variables as possible you'll need to find the sweet spot where the number of latent variables derived returns the most value let' put this into practice with small case study case studyfinding latent variables in wine quality data set in this short case studyyou'll use technique known as principal component analysis (pcato find latent variables in data set that describes the quality of wine then you'll compare how well set of latent variables works in predicting the quality of wine against the original observable set you'll learn how to identify and derive those latent variables how to analyze where the sweet spot is--how many new variables return the most utility--by generating and interpreting scree plot generated by pca (we'll look at scree plots in moment let' look at the main components of this example data set--the university of californiairvine (ucihas an online repository of data sets for machine learning exercises at we'll use the wine quality data set for red wines created by corteza cerdeiraf almeidat matosand reis it' , lines long and has variables per lineas shown in table you can find full details of the wine quality data set at