In data science, classifier performance measures the predictive capabilities of machine learning models with metrics like accuracy, precision, recall and F1 score. Nearly all metrics are based on the concepts of true and false predictions created by the model and measured against the actual outcomes. If the model is perfect, then all the predictions are true positive or true negative. Predictions that do not match the actual outcome are labeled false positives or false negatives.
Choosing the right classifier performance metric is important to data scientists evaluating different models and approaches. Choose classifier performance metrics based on the particular objectives for the model – to avoid false positives, or to avoid false negatives, or both. In a healthcare example, it would be important to minimize false negatives because illnesses could go undetected. In a spam detection example, it is important to minimize false positives to avoid discarding regular emails.
The C3 AI® Suite not only provides a rich library of classifiers for use in building enterprise AI applications, but a complete set of capabilities to simplify and accelerate the use of classifiers in enterprise AI applications. The C3 AI Suite provides an extensive curated library of machine learning algorithms, including numerous powerful and proven classifiers. The C3 AI Suite provides tools and capabilities that enable data scientists and developers to create their own custom classifiers.
Classifier algorithms are trained using labeled data as inputs. Training a classifier typically requires a significantly large set of labeled training data in order to achieve an acceptable level of precision. The C3 AI Suite provides extensive capabilities to simplify and accelerate classifier training and to test, tune, and validate classifier performance. Using C3 AI Suite’s powerful machine learning pipelines functionality, developers can rapidly and easily build sophisticated AI applications that employ multiple machine learning algorithms, including multiple classifiers, with far less code and complexity than other approaches.