Which process is essential for preparing your training dataset for machine learning?

Preparing a training dataset for machine learning is a multifaceted process, and each of the mentioned processes plays a crucial role in ensuring the dataset is effective for model training.

Data labeling is essential as it involves annotating the data with the correct output, which enables supervised learning algorithms to learn the mapping from inputs to outputs. Proper labeling is critical for the success of models that rely on accurate class information.

Data normalization is also a vital step, particularly when dealing with features that vary in scale. Normalizing data helps to standardize the range of independent variables or features, ensuring that they contribute equally to the analysis. This can improve the convergence speed of learning algorithms and lead to better model performance.

Data augmentation further enhances the dataset by artificially increasing its size through techniques such as rotation, shifting, and flipping of images or modifying existing data points. This helps models generalize better, especially when the original dataset is small or imbalanced.

All these processes work together to enhance the quality of the training dataset, making the chosen answer the most comprehensive and correct one. The integration of data labeling, normalization, and augmentation is essential in preparing a robust dataset capable of improving model accuracy and reliability.