Partial least squares regression, also referred to as PLS regression, is a form of predictive modelling technique used in the field of statistics and data science. It is most commonly used for predictive modelling, where it can help to reduce the complexity of data and improve the accuracy of the model. It is a multivariate regression method that is used to analyze a large data set with multiple variables.
PLS regression differs from other forms of predictive modelling techniques in that it is capable of handling data with high dimensionality. In other words, it can effectively analyze a data set that contains a large number of variables (such as customer survey data). It is also suitable for use in situations where there is a large difference between the number of independent variables and the number of dependent variables.
The basic idea behind PLS regression is to use Principal Component Analysis (PCA) to identify the most relevant variables to the model, and then use these variables to create a model that is able to best predict the outcome of the dependent variable. This is done by reducing the number of independent variables, while still keeping the most important variables in the model.
The process of PLS regression involves first creating a regression model using a smaller set of variables, and then using this model to determine which of the remaining variables should be included in the regression model. This process is repeated until all of the most important variables have been identified.
Once the model has been created, it can then be used to make predictions. This can be done by applying the model to a new data set, or by using it to predict the outcome of a future event.
Overall, PLS regression is seen as an effective tool for predictive modelling in the field of data science. As it is able to handle high dimensional data, it can provide more accurate predictions than other predictive modelling techniques.