There are many ways to measure the performance of a machine learning model. Below are some of the most common metrics used:
1. Accuracy: This is the most common metric used to measure the performance of a machine learning model. It is the ratio of correctly predicted observations to the total number of observations.
2. Precision: This metric measures the fraction of the predicted positive class that is actually correct. It is the ratio of correctly predicted positive observations to the total predicted positive observations.
3. Recall: This metric measures the fraction of actual positive class that is correctly predicted. It is the ratio of correctly predicted positive observations to all observations in actual class.
4. F1 Score: This metric is the harmonic mean of precision and recall. It is a measure of a model’s accuracy and precision.
5. ROC-AUC Curve: This metric is used to measure the performance of a binary classification model. It is the area under the receiver operating characteristic curve.
6. Mean Squared Error: This metric is used to measure the performance of a regression model. It is the average of the squares of the errors or deviations from the actual values.
7. Log Loss: This metric is used to measure the performance of a classification model. It is the negative log of the likelihood of the predicted values.