Classification
WorkFusion AutoML models provide an ability to automatically classify texts by analyzing its parts (tokens) and their combinations (features).
Binary Classification
Binary classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to)
Examples of Binary Classification:
- Dividend Announcements (is given text related or not related to dividends)
- Sentiment Analysis (positive or negative tweet analysis)
- Title Classification (compare two titles of a person and tell whether they match or not)
Manual Task in WorkFusion
Binary Classification can be done by human workers in WorkFusion:
- You provide a set of texts as a source.
- Workers need to read analyze the text and select one of two options (typically it is a radio button group): Yes or No (Valid or Invalid, Spam or Not Spam).
- Source texts and results (worker answers) are submitted to WorkFusion AutoML and Binary Classification ML models are trained on these data
Task 1
Task 2
Results
Results are stored in a separate column (match_result in the picture below):
Depending on your business problem, you might be more interested in a model that performs well for a specific subset of these metrics. For example, two business applications might have very different requirements for their ML models:
One application might need to be extremely sure about the positive predictions actually being positive (high precision) and be able to afford to misclassify some positive examples as negative (moderate recall).
Another application might need to correctly predict as many positive examples as possible (high recall) and will accept some negative examples being misclassified as positive (moderate precision).
Multi-Class Classification
Multi-class classification is very similar to the Binary Classification, the only difference is that you need to classify the elements of a given set into several groups (more than two).
Examples of Multi-class Classification:
- Product Description (which product does the given text describe: Computers, Food, Clothes, Books)
- Text Style Classification (identify the style of the given text: Romance, Thriller, Adventure, etc.)
- Company News (match given news to one of the companies in the list: Apple, Microsoft, or Intel)
Manual Task in WorkFusion
Task 1
Task 2
Results
