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Autopilot datasets and problem types

For tabular data (that is data in which each column contains a feature with a specific data type and each row contains an observation), Autopilot gives you the option of specifying the type of supervised learning problem available for the model candidates of the AutoML job, such as binary classification or regression, or of detecting it on your behalf based on the data you provide.

Autopilot datasets, data types, and formats

Autopilot supports tabular data formatted as CSV files or as Parquet files: each column contains a feature with a specific data type and each row contains an observation. The properties of these two file formats differ considerably.

The data types accepted for columns include numerical, categorical, text, and time series that consists of strings of comma-separated numbers. If Autopilot detects it is dealing with time series sequences, it processes them through specialized feature transformers provided by the tsfresh library. This library takes the time series as an input and outputs a feature such as the highest absolute value of the time series or descriptive statistics on autocorrelation. These outputted features are then used as inputs to one of the three problem types.

Autopilot supports building machine learning models on large datasets up to hundreds of GBs. For details on the default resource limits for input datasets and how to increase them, see Autopilot quotas.

Autopilot problem types

For the tabular data, you further specify the type of supervised learning problems available for the model candidates 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 labeled 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.