Random Baseline
Random Baseline is a statistical or machine learning methodology used as a simple reference point to evaluate the performance of predictive models. It involves generating predictions randomly, often based on the distribution of target variables in the training data, to establish a minimum performance threshold. This helps determine if a more complex model provides meaningful improvement over chance.
Developers should use Random Baseline when building and testing machine learning models to assess whether their models are learning useful patterns or just performing at random levels. It is crucial in classification and regression tasks to validate model efficacy, such as in A/B testing or academic research, ensuring resources are not wasted on ineffective algorithms. For example, in a binary classification problem, a random baseline might predict the majority class 50% of the time to set a benchmark.