Chapter 7. AutoML configuration parameters

7.1. Common parameters
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You set the following parameters for all task types.

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Table 7.1. Common parameters
Parameter	Description	Values
S3 connection	The data connection for your S3-compatible storage bucket.	Existing data connections in your project, or create a new connection.
Training data file	The CSV file containing your training data. Must use UTF-8 encoding and comma delimiters with a header row.	CSV format. Maximum 32 MiB when uploading through the dashboard, or 100 MB when loaded from S3.
Prediction task type	The type of prediction problem.	Binary classification, Multiclass classification, Regression, Time series forecasting
Top models to consider	The number of top-performing models to refit on the full training set.	1-10 (tabular) or 1-7 (time series), default 3

You set the following parameter for binary classification, multiclass classification, and regression tasks.

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Table 7.2. Tabular task parameters
Parameter	Description	Values
Label column	The column in your data set that contains the values to predict.	Populated from the CSV file schema.

You set the following parameters for time series forecasting tasks.

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Table 7.3. Time series task parameters
Parameter	Description	Values
Target column	The column with the numerical values to forecast.	Populated from the CSV file schema.
Timestamp column	The column with the date or time values.	Populated from the CSV file schema.
ID column	The column that identifies each time series.	Required. Populated from the CSV file schema.
Known covariates	Columns with values that are known in advance across the forecast horizon.	Optional. Populated from the CSV file schema.
Prediction length	The number of time steps to forecast.	Positive integer.

AutoML automatically selects the following during training. You cannot configure these values.

Algorithms and hyperparameters: AutoML uses AutoGluon to select and tune candidate models.
Train/test split: AutoML splits your data into training and test sets for evaluation.
Evaluation metric: AutoML selects the optimization metric based on your task type. For more information, see AutoML evaluation metrics.