Classification Models
Classification models are used to predict the category of a given input. They are used in a variety of applications, such as spam detection, sentiment analysis, and image recognition. Classification models are trained on labeled data, where each input is associated with a category label. The model learns to predict the category of new inputs based on the patterns it has learned from the training data.
Classification models can be binary or multiclass. Binary classification models predict one of two categories, while multiclass classification models predict one of multiple categories. The choice of model depends on the nature of the problem and the available data.
The TrueState platform provides a variety of classification models that can be used to build and deploy classification solutions. These model types are:
- Text Classification: Models that classify text inputs into categories.
- Record Classification: Models that classify structured data records (e.g. a row in an excel table) into categories.
Uses for Classification Models
Classification models are used in a wide range of applications, including:
Event prediction: Predicting whether an event will occur based on historical data. For example, churn modelling in customer retention. Churn models take historical context (e.g. customer interactions, purchase history) and predict whether a customer is likely to leave in the next time period (e.g. week, month). Sentiment analysis: Classifying text inputs (e.g. social media posts, reviews) into positive, negative, or neutral categories. Sentiment analysis is used to understand customer opinions and feedback. Process routing: Classifying structured data records (e.g. customer support tickets, purchase orders) into categories to route them to the appropriate department, team, agent or system.