Introduction to Machine Learning Models
More and more people have been wondering how computers can learn from examples. Think about teaching a kid how to recognize different fruits, for example, by displaying the images of apples, bananas, and oranges. The kid then learns to recognize the fruits with this set of examples. In the same way, machine learning systems use data to interpret information and make predictions. However, how do we know if the model is working well? This is a problem, which the machine learning model evaluation has to solve.
Why is Model Evaluation Important?
Evaluating a machine learning model is necessary to determine its effectiveness. The assessment is a lot similar to the one a teacher has to give when she evaluates her students. If the model is reliable and accurate, it can be counted on to make predictions. However, if the model is not accurate, there could be errors that might result in false decisions.
Common Ways to Evaluate Models
Accuracy
Accuracy is the most straightforward way to evaluate a model. It tells you the percentage of correct predictions the model made. For example, if your model predicts whether an email is spam or not, and it gets 90 out of 100 emails right, the accuracy is 90%.
Precision and Recall
These are a little more informative than precision. Precision deceits no matter how many of the model’s positive forecasts were actually correct. Relevance is no matter how many real positives were discovered by the model. For instance, if your model declares an email as spam (positive prediction), the ratio is how often it is telling the truth. The ratio of recall is how the machine is making mistakes in catching the real spam emails correctly.
Confusion Matrix
The confusion matrix is similar to the model’s results, acting as a report card for it. It demonstrates the exact count of both true and false predictions across all the sections. The matrix is named “confusion” because it is an instrument that assists in the comprehension of a model. One of the questions that you can ask is, for instance, how frequently does your model incorrectly equate spam emails with non-spam?
F1 Score
The F1 score is a combination of precision and recall. It gives a single score to understand how good the model is. If both precision and recall are high, the F1 score will also be high, indicating a good model.
ROC Curve and AUC
ROC curve, or in full “Receiver Operating Characteristic curve,” is a graph showing the model’s capability of detecting various classes, such as spam and non-spam emails. AUC (the Area Under the Curve) is the measure of the whole model’s performance. In case the AUC is near 1, so the model is very good.
How to Choose the Right Metric?
Choosing the right evaluation metric is widely dependent on the problem you’re trying to solve. In case you are aiming at catching all spam emails, then the recall can be the key factor. Nevertheless, if you want to avoid false positives like non-spams emails labeled as spam, then it is also important to excel in precision. Occasionally, the application of a few different metrics will give better insights.
Conclusion
Evaluating machine learning models is just like checking on how properly a recipe is made. It will help you see the places where you did a good job and the ones where your model had some bumps. It will allow you to ensure that your model is functioning well and it can make the right predictions. This guide ought to give you a basic perception of how you can start measuring machine learning models in a very simple and comprehensible way.
