ML Model Evaluation
- [Beautiful Mountain River - Lexi Brewere]
- Overview
Model evaluation in machine learning is the process of assessing a machine learning (ML) model's performance, strengths, and weaknesses. It's important for determining if a model is useful, accurate, and reliable, and for helping organizations achieve their business goals.
Here are some key aspects of model evaluation:
- Predictive accuracy: How well a model can make accurate predictions on new data.
- Generalization: The ability of a model to make good predictions on
- new, unseen data.
- Evaluation metrics: Quantitative metrics like F1 score or RMSE, or qualitative assessments by subject matter experts.
- Evaluation methods: Splitting data into training and testing sets, and training the model on the training set before evaluating it on the testing set.
- Evaluation timing: Model evaluation can be performed offline during experimentation or continuous retraining, or online as part of model monitoring.
- Evaluation goals: The metrics chosen should reflect the business metrics that the machine learning solution is intended to optimize.
In ML, model evaluation involves measuring the predictive accuracy of a model. Predictive accuracy refers to how well a model can make accurate predictions on unseen data.
[More to come ...]
