Which phase in machine learning typically includes framing the objectives and defining the problem?

The phase in machine learning that includes framing the objectives and defining the problem is the problem definition phase. During this critical stage, the focus is on understanding the specific needs of the project, determining what problem needs to be solved, and establishing clear objectives that the machine learning model must achieve.

Defining the problem sets the foundation for the entire machine learning workflow as it influences subsequent steps such as data collection, feature selection, and method selection. This phase ensures that the efforts in building the model align with the desired outcomes, allowing for effective communication with stakeholders and guiding the model development process.

The other phases such as model evaluation, data preprocessing, and model deployment are vital components of the machine learning lifecycle, but they are focused on different aspects. Model evaluation assesses the performance of a trained model, data preprocessing involves preparing and cleaning the data, and model deployment refers to the integration of the model into a production environment to provide real-time predictions.