Model Task Types

Fiddler currently supports four model tasks. These include:

• Binary Classification
• Multi-class Classification
• Regression
• Ranking

Binary classification is the task of classifying the elements of an outcome set into two groups (each called class) on the basis of a classification rule. Typical binary classification problems include:

• Determining whether a customer will churn or not. Here the outcome set has two outcomes: The customer will churn or the customer will not. Further, the outcome can only belong to either of the two classes.
• Determining whether a patient has a disease or not. Here the outcome set has two outcomes: the patient has the disease or does not.

Multiclass classification is the task of classifying the elements of an outcome set into three or more groups (each called class) on the basis of a classification rule. Typical multiclass classification problems include:

• Determining whether an image is a cat, a dog, or a bird. Here the outcome set has more than two outcomes. Further, the image can only be determined to be one of the three outcomes and it's thus a multiclass classification problem.

Regression is the task of predicting a continuous numeric quantity. Typical regression problems include:

• Determining the average home price based on a given set of housing related features such as it's square footage, number of beds and bath, it's location etc.
• Determining the income of an individual based on features such as their age, work location, their job sector etc.

Ranking is the task of constructing a rank ordered list of items given a particular query that seeks some information. Typical ranking problems include:

• Ranking documents in information retrieval systems.
• Ranking relevancy of advertisements based on user search queries.