Binary classification for tabular data

Classification machine learning algorithms are used to categorize data into specific classes or categories based on a set of features. The goal of a classification model is to accurately predict the class or category of new, unseen data.

There are two main types of classification models: binary classification and multiclass classification. Binary classification is used to classify data into two categories, such as spam or not spam, while multiclass classification is used to classify data into more than two categories, such as customer segmentation or plant species.

Classification models can be used in a variety of applications, such as spam detection, fraud detection, sentiment analysis, medical diagnosis, customer segmentation, and object recognition.

The performance of a classification model can be evaluated using several metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrix. These metrics help to determine the model’s ability to correctly classify data and distinguish between different classes.

VIANOPS 2.1 has full support for binary classification models for tabular data that covers:

• Performance drift monitoring
• Feature drift monitoring
• Prediction drift monitoring
• Bias & fairness analysis and monitoring

Performance metrics tracked in VIANOPS: accuracy, balanced accuracy, precision, recall, F1-score, and confusion matrix.

Go to the Monitor your model How-To guide to learn more details about how to set up monitoring jobs with policies and view monitoring dashboards.

Also, review this blog example of monitoring and analyzing a classification model.