Validation in machine learning data preprocessing is crucial for several reasons. It helps ensure the quality and reliability of the dataset, which directly impacts model performance. Without validation, models may be trained on inaccurate or irrelevant data, leading to poor predictions.
Validation also aids in identifying outliers and anomalies that could skew results. This process can highlight errors or inconsistencies in the data collection phase, allowing them to be addressed before training begins.
Moreover, validation assists in preventing overfitting, a common issue in machine learning where a model performs well on training data but poorly on unseen data. By using techniques like cross-validation, we can estimate how our model will perform on new data, enabling us to fine-tune it accordingly.
