Several measures can be used to evaluate a machine learning model’s performance and these metrics will change based on the job at hand and the kind of model being evaluated. **F1 score**,** accuracy**,** precision**,** recall**, and** area under the ROC curve** are some of the most used measures used to assess machine learning models.

## Evaluation metrics for modelâ€™s accuracy

**The F1 score**: This is a combined accuracy measure that is the harmonic mean of recall and accuracy.

**Accuracy**: One of the most popular ways machine learning models are measured is by their degree of accuracy. It is the fraction of times the model got the answer right as a percentage. If a model can properly predict 90 out of 100 cases, for instance, its accuracy would be 90%. However, in cases with unbalanced datasets when one class is substantially more common than the other, accuracy might be deceptive.

**Precision and recall**: The accuracy of a model is measured by how many of its favorable predictions come to fruition. The recall of a model is the percentage of correctly predicted positive cases relative to the total number of positive instances. If the model has a low false-positive rate (high accuracy), then it also has a low false-negative rate (high recall).

**AUC-ROC**: The accuracy of a binary classifier is evaluated by its capacity to distinguish between positive and negative classes, and this is measured by the area under the receiver operating characteristic curve. A ROC curve plots the proportion of correct diagnoses versus the number of incorrect ones (FPR). Itâ€™s a measure of performance. A perfect model would have an AUC-ROC of 1, whereas a random model would have a 0.

**Mean squared error (MSE)** for regression tasks and **log loss or cross-entropy** for classification tasks are two additional metrics that may be used to assess machine learning models.

## Efficacy of ML model

**The job at hand and the nature of the data must be taken into account when judging the efficacy of a machine learning model.** In a binary classification assignment, it is crucial to assess not just the model’s accuracy, but precision and recall as well. Mean squared error may be a better measure to use when monitoring models during a regression job.

When you evaluate the ML model, consider both the broader and more particular context. A model’s success on one dataset is no guarantee of success on another or in a different setting. Since machine learning models may be used for a wide range of applications, it is necessary to employ multiple metrics to assess their effectiveness and to keep in mind the context of the work at hand while interpreting the findings.