Term
Preprocessing (ˈpriːˌprəʊˈsɛsɪŋ)
Definition
Preprocessing in AI refers to preparing and cleaning input data before it is used to train a model. This process ensures that the data is in the best possible shape to help the model learn effectively.
Where you’ll find it
In AI platforms, preprocessing tools and settings are typically located in the data management or model training sections. Libraries like TensorFlow and Scikit-learn commonly feature these tools, available across all their versions.
Common use cases
- Removing errors or irrelevant data from your dataset before training begins.
- Converting data into a format or scale that the algorithm can easily process.
- Segmenting complex data into more manageable parts for effective analysis.
Things to watch out for
- Over-cleaning data can remove useful variability, potentially leading to a less effective model.
- Different data types may require unique preprocessing approaches; applying the wrong method can skew results.
- Always validate the model post-preprocessing to ensure the integrity of the data has been maintained.
Related terms
- Data Cleansing
- Feature Scaling
- Model Training
- TensorFlow
- Scikit-learn