AI has been impressive in a number of ways. One of the most impressive feats AI has achieved is its ability to identify objects and faces. For example, Google’s AI software can identify a cat with up to 75% accuracy. AI also has the ability to learn and improve over time with more data. This learning capability is what allows for things like driverless cars and facial recognition to continue to get better over time.
Evaluation methods in machine learning are used to assess the accuracy of predictions made by a model. There are many different evaluation metrics, but some common ones include accuracy, precision, recall, and F1 score.
To choose the appropriate evaluation metric for your task, you need to understand the trade-offs between them. For example, accuracy is very intuitive and easy to understand, but it can be misleading if there is a class imbalance in your data (i.e., one class is much more represented than another). In that case, you might want to use precision or recall instead.
Another considerations when choosing an evaluation metric is whether you care more about false positives or false negatives. For example, if you are building a model to detect fraudsters on a website, you care more about false negatives (i.e., fraudsters that are not detected) than false positives (i.e., non-fraudsters that are incorrectly flagged as fraudsters). In that case, you would want to use a metric like recall which penalizes false negatives more than false positives.