Choosing the right evaluation metrics for unsupervised learning can be tricky. Here are some main challenges:
Lack of Ground Truth: In unsupervised learning, we don’t have labels or clear answers. This makes it hard to judge how well the model is doing, since we have to make assumptions.
Metric Limitations: Some common metrics, like the Silhouette Score and the Davies-Bouldin Index, might not tell the whole story about how good a clustering is.
To handle these challenges, here are a couple of suggestions:
Use Multiple Metrics: Using different metrics together can help give a better overall view of how well the model is performing.
Domain Knowledge: It’s important to use what you know about the specific area you’re working in. This can help you pick the best metric for your particular needs.
Choosing the right evaluation metrics for unsupervised learning can be tricky. Here are some main challenges:
Lack of Ground Truth: In unsupervised learning, we don’t have labels or clear answers. This makes it hard to judge how well the model is doing, since we have to make assumptions.
Metric Limitations: Some common metrics, like the Silhouette Score and the Davies-Bouldin Index, might not tell the whole story about how good a clustering is.
To handle these challenges, here are a couple of suggestions:
Use Multiple Metrics: Using different metrics together can help give a better overall view of how well the model is performing.
Domain Knowledge: It’s important to use what you know about the specific area you’re working in. This can help you pick the best metric for your particular needs.