Presented by Jorge Anicama and Janine Rafalko
A Data Model is the key output part of a Machine Learning (ML) process. It helps the prediction of new outcomes as new values are inserted into it. But, how can we make certain if we have the right data model? Each ML technique is associated with metrics that evaluates the ML data model performance. This presentation will answer this using Oracle Analytics Cloud (OAC) as an example.
This presentation will demonstrate the most commonly used ML techniques and algorithms, and their corresponding Data Model evaluation metric. Then, we will use Oracle Analytics Cloud (OAC) to provide a comparison by using a data set (Titanic or Cancer- dataset).
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