Which of the following can be a consequence of insufficient exploratory data analysis?

Insufficient exploratory data analysis (EDA) can lead to a range of issues in the modeling process, making the selection of the option "all of the above" particularly relevant.

When EDA is not thoroughly conducted, it can result in increased model complexity. This happens because without understanding the data's distribution, relationships, and underlying patterns, a data scientist may opt for more complex models in an attempt to capture those unknowns, ultimately overcomplicating the model unnecessarily.

Additionally, lacking a robust EDA can lead to underfitting in predictive modeling. Underfitting occurs when a model is too simplistic to capture the underlying trend of the data. Without proper analysis, critical features may be overlooked, resulting in a model that fails to learn adequately from the data, leading to poor predictive performance.

Lastly, poor choice of model parameters can stem from insufficient EDA. Understanding the data and its intricacies is essential for selecting appropriate parameters that influence the model's ability to learn and generalize. If a data scientist does not grasp the significance of variables or the relationships between them, this can lead to suboptimal parameterization, further impacting model performance.

Overall, thorough exploratory data analysis plays a critical role in informing model choice, complexity, and the selection