When judging how well a supervised learning model works, accuracy by itself can be confusing. Here’s why:
Imbalanced Datasets: Sometimes, a dataset has a lot more examples of one type than others. In this case, a model might seem very accurate just because it keeps predicting the most common type.
Precision and Recall: These terms are super important, especially in fields like healthcare. If the model misses something important, like a disease, it can have serious effects.
F1 Score: This score helps combine precision and recall. It gives a better overall view of how the model is doing.
So, it's important to look at more than just accuracy to really understand how well a model is performing!
When judging how well a supervised learning model works, accuracy by itself can be confusing. Here’s why:
Imbalanced Datasets: Sometimes, a dataset has a lot more examples of one type than others. In this case, a model might seem very accurate just because it keeps predicting the most common type.
Precision and Recall: These terms are super important, especially in fields like healthcare. If the model misses something important, like a disease, it can have serious effects.
F1 Score: This score helps combine precision and recall. It gives a better overall view of how the model is doing.
So, it's important to look at more than just accuracy to really understand how well a model is performing!