SPSS Syntax has long been exploited by expert analysts due to its flexibility, power and ease of learning. Syntax vastly increases users’ productivity by making it easier to automate commonly used procedures.
In this two-part video series Jarlath Quinn explores how to work with the Neural Networks module in SPSS Statistics (watch part one here) Part 2: Shows how to create a partition variable to control which cases are used in Training and Testing Explores the effect of choosing different activation functions Explains and demonstrates different model training …
In this two-part video series Jarlath Quinn explores how to work with the Neural Networks module in SPSS Statistics. Watch part two here. Part 1: Introduces the concept of Neural Networks Shows how to build a basic Neural Network model to predict credit worthiness Explains the how to interpret the default output generated by the procedure
In classical statistics, linear regression is regarded as the ‘gateway to predictive modelling’. For decades students have been taught about regression from theory to practice simply because it still one of the most versatile and simple ways to understand and predict the effect of key factors on critical outcomes.
Feature Engineering is really just a fancy term for creating new data. Very often we can help an algorithm build better models by preparing the input data in a way that allows it to detect a clearer signal in the often noisy data. In machine learning variables are often referred to as ‘features’, so feature engineering refers to the transformation of variables or the creation of new ones.
The idea of meta modelling is to build a predictive model using the predictions or scores generated by another model. By adding the predictive scores generated by an initial modelling algorithm to an existing pool of predictor fields, a second algorithm can then exploit these scores in to build a final more accurate model.
Split models or split population modelling is another technique that allows the user to build multiple models which can then be combined to create a single prediction. The idea with split modelling is that if the data represent different populations or contain separate groups that behave in very different ways, assuming that a single model can explain all the inherent variability across these distinct populations might be unreasonable.
In this video Jarlath Quinn demonstrates how to merge data files within SPSS Statistics using each of the two main methods, either adding cases (combining files with the same fields but additional rows) or adding variables (combining files by joining variables to a target file using something like an ID field as a ‘keyed variable’).