Data quality isn't just a technical issue: It impacts an organization's compliance, operational efficiency, and customer satisfaction.
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Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong. The wrong features will give you inaccurate answers and may impact your ML models’ efficiency in ways you can’t predict. Focusing on establishing a reliable feature selection process will pay dividends when you move your models into production and have less margin for error in your predictions. More importantly, prioritizing feature stability and explainability will make your models more future-proof.
This guide breaks down:
• How to start thinking about feature selection the right way
• Some of the most popular and valuable selection methods
• Why you need to account for feature stability in your models
