When people look at how well machine learning models work, they often think about accuracy first. Accuracy is simply the percentage of correct predictions made by the model compared to the total number of predictions. While accuracy is a good starting point, relying only on it can be confusing. Let’s explore why using only accuracy might not give the full picture.
One big problem with using accuracy is when the data isn’t balanced.
For example, imagine you’re trying to create a model that predicts if an email is spam or not. If 95% of your emails are not spam and only 5% are, your model could predict every email as "not spam" and still have 95% accuracy! But this wouldn’t be helpful because it wouldn’t catch any spam emails.
Accuracy does not show the difference between different kinds of mistakes. For instance, in health checks, mistaking a healthy person for someone sick (false positive) is not the same as missing an actually sick person (false negative).
In cases like these, two terms become important: precision and recall. Precision tells us how many of the cases that the model said were positive were actually positive. Recall shows how many actual positive cases the model correctly identified.
Accuracy doesn't explain how well the model works on different groups. This is important, especially when the costs of mistakes are different.
Let’s say we’re predicting if someone will default on a loan. If we mistakenly identify a good loan applicant as a risk (false positive), they might miss out on a loan. On the other hand, if we fail to catch a bad loan applicant (false negative), it could lead to financial loss. In such ways, metrics like the F1 score, which combines precision and recall, give a clearer idea of how well the model is really performing.
Accuracy can change a lot with small changes to the dataset.
For instance, if you add more examples from the larger group of emails, the accuracy might look better, even if the model still struggles with identifying spam.
To wrap things up, while accuracy is helpful, it shouldn't be the only metric to look at. Using other measures like precision, recall, F1 score, and ROC-AUC can give you a better view of how your model is doing. This way, you can ensure your model not only performs well overall but also meets the specific needs of your project. Using a variety of performance metrics will make you a better machine learning expert!
When people look at how well machine learning models work, they often think about accuracy first. Accuracy is simply the percentage of correct predictions made by the model compared to the total number of predictions. While accuracy is a good starting point, relying only on it can be confusing. Let’s explore why using only accuracy might not give the full picture.
One big problem with using accuracy is when the data isn’t balanced.
For example, imagine you’re trying to create a model that predicts if an email is spam or not. If 95% of your emails are not spam and only 5% are, your model could predict every email as "not spam" and still have 95% accuracy! But this wouldn’t be helpful because it wouldn’t catch any spam emails.
Accuracy does not show the difference between different kinds of mistakes. For instance, in health checks, mistaking a healthy person for someone sick (false positive) is not the same as missing an actually sick person (false negative).
In cases like these, two terms become important: precision and recall. Precision tells us how many of the cases that the model said were positive were actually positive. Recall shows how many actual positive cases the model correctly identified.
Accuracy doesn't explain how well the model works on different groups. This is important, especially when the costs of mistakes are different.
Let’s say we’re predicting if someone will default on a loan. If we mistakenly identify a good loan applicant as a risk (false positive), they might miss out on a loan. On the other hand, if we fail to catch a bad loan applicant (false negative), it could lead to financial loss. In such ways, metrics like the F1 score, which combines precision and recall, give a clearer idea of how well the model is really performing.
Accuracy can change a lot with small changes to the dataset.
For instance, if you add more examples from the larger group of emails, the accuracy might look better, even if the model still struggles with identifying spam.
To wrap things up, while accuracy is helpful, it shouldn't be the only metric to look at. Using other measures like precision, recall, F1 score, and ROC-AUC can give you a better view of how your model is doing. This way, you can ensure your model not only performs well overall but also meets the specific needs of your project. Using a variety of performance metrics will make you a better machine learning expert!