Using different ways to check how good a model is helps a lot in supervised learning. If we only look at one measure, we might misunderstand how well the model is doing. Different measures show us different parts of how the model works.
For example, accuracy gives a quick idea of how many predictions were right compared to all the predictions made. But if the classes are not balanced, accuracy can be tricky. A model that mostly guesses the popular class might seem like it's doing great because it has high accuracy, but it could be failing at predicting the less common class.
That’s where precision and recall become important. Precision looks at how many of the positive predictions were actually correct. It answers this question: Of all the positive predictions we made, how many were right?
On the other hand, recall checks how good the model is at finding all the real positive cases. It asks: Of all the actual positives out there, how many did we predict accurately?
The F1 score combines precision and recall into one score. This helps balance the two measures. It’s especially useful when the classes are uneven, so we don’t focus too much on just one of them.
Another important measure is the ROC-AUC score. This shows the trade-off between correctly predicting positives and incorrectly predicting positives. A high AUC means the model is good at telling apart the positive and negative classes in different situations.
In short, using several evaluation measures gives us a clearer picture of how a model is performing. It stops us from relying too much on just one measure and ensures we look at important details like class imbalance and the balance between precision and recall. This well-rounded way of looking helps us make better choices when picking and improving models.
Using different ways to check how good a model is helps a lot in supervised learning. If we only look at one measure, we might misunderstand how well the model is doing. Different measures show us different parts of how the model works.
For example, accuracy gives a quick idea of how many predictions were right compared to all the predictions made. But if the classes are not balanced, accuracy can be tricky. A model that mostly guesses the popular class might seem like it's doing great because it has high accuracy, but it could be failing at predicting the less common class.
That’s where precision and recall become important. Precision looks at how many of the positive predictions were actually correct. It answers this question: Of all the positive predictions we made, how many were right?
On the other hand, recall checks how good the model is at finding all the real positive cases. It asks: Of all the actual positives out there, how many did we predict accurately?
The F1 score combines precision and recall into one score. This helps balance the two measures. It’s especially useful when the classes are uneven, so we don’t focus too much on just one of them.
Another important measure is the ROC-AUC score. This shows the trade-off between correctly predicting positives and incorrectly predicting positives. A high AUC means the model is good at telling apart the positive and negative classes in different situations.
In short, using several evaluation measures gives us a clearer picture of how a model is performing. It stops us from relying too much on just one measure and ensures we look at important details like class imbalance and the balance between precision and recall. This well-rounded way of looking helps us make better choices when picking and improving models.