We currently have 10 entries in this glossary.
Bias – Systematic errors in AI models that lead to unfair or inaccurate predictions.
Big Data – Large, complex datasets that require advanced analytics techniques.
Dimensionality Reduction – Techniques like PCA that reduce the number of input variables in a model.
Embeddings – A representation of data (e.g., words or images) in a lower-dimensional space to capture relationships.
Feature Engineering – The process of selecting and transforming variables to improve model performance.
Overfitting – When an ML model learns noise instead of patterns, performing well on training data but poorly on new data.
Test Data – Data used to evaluate the performance of an AI model.
Training Data – Data used to teach an AI model how to recognize patterns.
Underfitting – When an ML model is too simple to capture the underlying patterns in data.
Variance – A model’s sensitivity to fluctuations in training data, leading to overfitting.
