Jarlath Quinn

A veteran of the predictive analytics industry working under the auspices of SAS, IBM and SPSS, Jarlath is pre-sales director at Smart Vision Europe. He is one of the most experienced SPSS trainers in the industry.

6 secrets of building better models part three: feature engineering

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.

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6 secrets of building better models part six: split models

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.

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How to combine variables in SPSS Statistics

SPSS users often want to know how they can combine variables together. In this video Jarlath Quinn demonstrates how to use the compute procedure to calculate the mean of a number of variables to create one combined variable, and also how to use the count values procedure to count how many times a particular value occurs across a series of variables in order to create an overall count.

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