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Model Training

A phase of the machine learning lifecycle during which data scientists apply an ML algorithm to historical data to generate the desired model accuracy. This is an iterative process in which they review model results and then adjust features, tweak model parameters such as weights, or change ML techniques or algorithms, as needed.

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