Amazon SageMaker Autopilot problem types - Amazon SageMaker

Amazon SageMaker Autopilot problem types

When setting a problem type, such as binary classification or regression, with the AutoML API, you have the option of specifying it or of letting Amazon SageMaker Autopilot detect it on your behalf. You set the type of problem with the CreateAutoPilot.ProblemType parameter. This limits the kind of preprocessing and algorithms that Autopilot tries. When the job is finished, if you had set the CreateAutoPilot.ProblemType, then the ResolvedAttribute.ProblemType will match the ProblemType you set. If you leave it blank (or null), the ProblemType will be whatever Autopilot decides on your behalf.

Note

In some cases, Autopilot is unable to infer the ProblemType with high enough confidence, in which case you must provide the value for the job to succeed.

Your problem type options are as follows:

Regression

Regression estimates the values of a dependent target variable based on one or more other variables or attributes that are correlated with it. An example is the prediction of house prices using features like the number of bathrooms and bedrooms, square footage of the house and garden. Regression analysis can create a model that takes one or more of these features as an input and predicts the price of a house.

Binary classification

Binary classification is a type of supervised learning that assigns an individual to one of two predefined and mutually exclusive classes based on their attributes. It is supervised because the models are trained using examples where the attributes are provided with correctly labelled objects. A medical diagnosis for whether an individual has a disease or not based on the results of diagnostic tests is an example of binary classification.

Multiclass classification

Multiclass classification is a type of supervised learning that assigns an individual to one of several classes based on their attributes. It is supervised because the models are trained using examples where the attributes are provided with correctly labelled objects. An example is the prediction of the topic most relevant to a text document. A document may be classified as being about, say, religion or politics or finance, or about one of several other predefined topic classes.