Model Data
Model Data
After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.
Keywords: machine learning pipelines, automated machine learning, AutoML, Driverless AI, features, descriptive statistics, LogLoss, cross validation, learning curves, model speed, machine learning blueprints, imputation, one-hot encoding, regression, classification, model evaluation, ROC, F1-score, confusion matrix, DataRobot
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