How does data quality affect machine learning models?

How does data quality affect machine learning models?

Data quality is crucial for machine learning models as it directly influences their accuracy and performance. Poor quality data can lead to biased results, overfitting, and ultimately, unreliable predictions.

What are some common data quality issues?

Common data quality issues include missing values, duplicates, inconsistencies, and outliers. Each of these can skew the model's learning process.

How can I improve data quality for my ML project?

You can improve data quality by implementing data cleaning techniques, validating data sources, and using robust data collection methods.

What tools can help with data quality assessment?

There are several tools available, such as Talend, Apache Nifi, and OpenRefine, which can help assess and improve data quality.

Can you give an example of how poor data quality impacted a real-world ML model?

Certainly! One notable example is the use of biased training data in facial recognition systems, which led to higher error rates for certain demographic groups.