In machine learning, what is typically the first step before training a model?

The first step typically taken before training a machine learning model is data preprocessing and cleaning. This step is crucial because the quality and structure of the data directly impact the performance of the machine learning models. During this phase, data is prepared for analysis by identifying and rectifying issues such as missing values, outliers, or irrelevant features. It may also involve transforming the data into a format that is consistent and usable for the model, such as normalizing or standardizing numerical values.

By ensuring that the data is clean and properly formatted, model training can proceed more effectively, as models trained on well-prepared data are more likely to generalize well to new, unseen data. This foundational step lays the groundwork for all subsequent tasks in the machine learning pipeline.