Overview
Model evaluation and architecture are fundamental aspects of machine learning that ensure models are effective and reliable. By understanding various evaluation metrics and the importance of model architecture, learners can make informed decisions about model design and performance assessment. This ...
Key Terms
Example: Model evaluation helps determine if a model is ready for deployment.
Example: An accuracy of 90% means 90 out of 100 predictions were correct.
Example: High precision indicates that most positive predictions are correct.
Example: High recall means the model identifies most of the actual positives.
Example: An F1 score of 0.8 indicates a good balance between precision and recall.
Example: An overfitted model performs well on training data but poorly on new data.