What does the experimentation step in the Data Science workflow primarily involve?

The experimentation step in the Data Science workflow is primarily focused on the process of refining and optimizing models through parameter adjustments. Hyperparameter tuning specifically involves modifying the variables that control the learning process of a model, such as learning rates, depth of trees in a decision tree, or the number of clusters in a clustering algorithm. The goal during experimentation is to identify the optimal set of hyperparameters that improve the model's performance and accuracy on unseen data.

This iterative process allows data scientists to test different configurations and validate their effectiveness, ultimately leading to a more robust and effective model. Tuning hyperparameters can significantly impact how well a model learns from the training data and generalizes to new data, making this step crucial in building effective predictive models.

The other options are related to different aspects of the data science workflow. For instance, repeated model deployment speaks to the operationalization of models, data cleaning operations involve preparing the dataset for analysis, and visualization of model outcomes pertains to the interpretation and communication of results rather than the experimentation phase itself. Each of these plays an important role in the overall data science process but does not define the primary focus of the experimentation step.