Evaluating supervised learning models is really important. We want to make sure they work well on new data that they haven’t seen before. In supervised learning, we mainly focus on two types of problems: classification and regression. Each of these has its own ways to measure how well they’re doing.
When we talk about classification, we’re trying to predict categories or groups. Here are some common ways to measure how good the predictions are:
Accuracy: This is the easiest way to check performance. It tells us the ratio of correct predictions to the total number of predictions.
Precision: This measures how many of the predicted positive cases were actually correct.
Recall (Sensitivity): This checks how well the model finds all the positive cases.
F1 Score: This combines precision and recall into one number. It’s helpful when we have uneven classes.
For regression, we are predicting continuous values like prices or temperatures. Here are some ways we measure how good these predictions are:
Mean Absolute Error (MAE): This calculates the average of the differences between the predicted and real values, ignoring whether they are over or under.
Mean Squared Error (MSE): This squares the differences before averaging, which makes larger errors count more.
R-squared: This shows how much of the change in the outcome can be explained by the model.
Choosing the right way to measure a model is really important. It affects which model we pick and how we improve it. For example, in a medical diagnosis, finding all positive cases (high recall) might be more critical than getting a few wrong (precision). By understanding these different measures, data scientists can make smart choices, check their models better, and get useful results.
Evaluating supervised learning models is really important. We want to make sure they work well on new data that they haven’t seen before. In supervised learning, we mainly focus on two types of problems: classification and regression. Each of these has its own ways to measure how well they’re doing.
When we talk about classification, we’re trying to predict categories or groups. Here are some common ways to measure how good the predictions are:
Accuracy: This is the easiest way to check performance. It tells us the ratio of correct predictions to the total number of predictions.
Precision: This measures how many of the predicted positive cases were actually correct.
Recall (Sensitivity): This checks how well the model finds all the positive cases.
F1 Score: This combines precision and recall into one number. It’s helpful when we have uneven classes.
For regression, we are predicting continuous values like prices or temperatures. Here are some ways we measure how good these predictions are:
Mean Absolute Error (MAE): This calculates the average of the differences between the predicted and real values, ignoring whether they are over or under.
Mean Squared Error (MSE): This squares the differences before averaging, which makes larger errors count more.
R-squared: This shows how much of the change in the outcome can be explained by the model.
Choosing the right way to measure a model is really important. It affects which model we pick and how we improve it. For example, in a medical diagnosis, finding all positive cases (high recall) might be more critical than getting a few wrong (precision). By understanding these different measures, data scientists can make smart choices, check their models better, and get useful results.