Understanding Evaluation Metrics in Machine Learning

When we talk about learning with computers, there are two main ways: supervised learning and unsupervised learning.

Supervised Learning

In supervised learning, we have clear labels that tell us what to look for. This makes it easier to check how well our model is doing. Some important terms we use are:

• Accuracy: How often the model gets things right.
• Precision: How many of the things the model marked as true are actually true.
• Recall: How well the model finds all the true cases.

These measures help us see how good our model is at predicting the results based on what it learned.

Unsupervised Learning

Now, unsupervised learning is different. It doesn’t have those labels to help us out. Instead, we look at how things group together or relate to each other. Here are three common ways we measure this:

• Silhouette Score: This tells us how close an item is to its own group compared to other groups.
• Davies-Bouldin Index: This looks at how similar each group is to the best-matched group.
• Inertia: Used in a method called K-means, this checks how tightly grouped the items are in a cluster.

In a nutshell, supervised learning uses clear goals, while unsupervised learning focuses on finding patterns in the data itself.

Knowing about these different metrics can really help us use machine learning in smarter ways!