Which of the following is true about data preparation in machine learning?

Data preparation is a critical phase in the machine learning pipeline that directly impacts the performance of the model. The statement that it ensures the model can learn from relevant and clean data is accurate, as this process involves a variety of steps to transform raw data into a format suitable for analysis. Proper data preparation includes cleaning the data to eliminate inaccuracies and inconsistencies, transforming features for better model compatibility, and selecting relevant subsets of data that are essential for model training.

The steps taken during data preparation, such as normalization, encoding categorical variables, and handling missing values, help in creating a dataset that accurately represents the underlying patterns needed for successful machine learning outcomes. When data is well-prepared, it allows the model to learn effectively, leading to more reliable predictions and insights.

In contrast, the other options do not fully encompass the importance and comprehensiveness of data preparation. It is far from optional; skipping this critical step often leads to poor model performance. Additionally, data preparation is necessary for datasets of all sizes, not just large ones, as even small datasets can contain noise and irrelevant features that need to be addressed. Finally, data preparation involves much more than just cleaning; it encompasses multiple processes that enhance dataset quality, making the statement about it involving only data cleaning