What is the primary objective of data pre-processing in machine learning?

The primary objective of data pre-processing in machine learning is to clean and normalize data to remove noise and prepare for training. This step is crucial because the quality of the data directly impacts the performance of the machine learning model.

During pre-processing, various techniques are employed to address issues such as missing values, inconsistent formatting, and outliers, ensuring that the dataset is reliable and suitable for analysis. Normalization techniques, such as scaling or transforming data to a common range, help to improve the convergence of learning algorithms and make the training process more efficient.

By ensuring that the data is well-structured and free from irregularities, pre-processing lays the foundation for effective model training, leading to more accurate predictions and a better overall performance of machine learning systems. The focus here is on data integrity and readiness for model consumption, distinguishing it from the other options that do not address this fundamental aspect of machine learning.