The Impact of Raw Data on Model Performance
ML algorithms are designed to learn patterns from data. However, when the input data is flawed—whether due to missing data, outliers, or irrelevant features—the model's ability to generalize from training data on unseen data diminishes. For instance, a model trained on “noisy” or biased data may yield inaccurate predictions, leading to poor decision-making in real-world applications like the examples given previously.
Consider a simple classification task where the dataset contains missing data. If these values are not addressed through appropriate preprocessing techniques, the model may either ignore the affected instances or make erroneous assumptions about the missing data. This can result in a skewed understanding of the underlying patterns, ultimately degrading model performance.
Common Data Issues
Some of the most common instances of data quality issues in ML model development include the following:
- Missing...
