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In Phase 3, the data science team identifies candidate models to apply to the data for clustering, classifying, or finding relationships in the data depending on the goal of the project, It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became acquainted with the data and understanding the business problems or domain area. These hypotheses help the team frame the analytics to execute in Phase 4 and select the right methods to achieve its objectives.

Some of the activities to consider in this phase include the following: Assess the structure of the datasets. The structure of the datasets is one factor that dictates the tools and analytical techniques for the next phase. Depending on whether the team plans to analyze textual data or transactional data, for example, different tools and approaches are required.

Ensure that the analytical techniques enable the team to meet the business objectives and accept or reject the working hypotheses. Determine if the situation warrants a single model or a series of techniques as part of a larger analytic workflow. A few example models include association rules and logistic regression Other tools, such as Alpine Miner, enable users to set up a series of steps and analyses and can serve as a front-end user interface (Ul) for manipulating Big Data sources in PostgreSQL.

Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.

The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.

The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.

Support vector count and cluster density do not apply to recommender systems.

MAE and AUC are both valid and useful metrics for measuring recommender systems.