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AutoAI feature comparison
AutoAI feature comparison

AutoAI feature comparison

Configuration options for AutoAI experiments differ based on the type of experiment you build. This table notes the differences between options for various types of AutoAI experiments.

Experiment type

An AutoAI experiment can be one of these types:

  • Binary classification, multiclass classification, or regression model trained by using a single data source.
  • Binary classification, multiclass classification, or regression model that is built from joining multiple data sources to train the experiment.
  • Univariate or multivariate time series forecast experiment.

Feature support and configuration options by experiment type

Feature or option Single data source Joined data Time series
Max number of data files 1 4 1
Testing data file
Training data limits 1 GB 4 GB per file, 20 GB total Max of 10 columns, 300,000 rows
Define holdout size ✓ (percent in the range 85 - 95%) ✓ (percent 85 - 95%) ✓ (as number of rows between 1 and dataLength/2)
Max number of classes 200 200
Drop Duplicate Rows
Subsample data
Exclude columns from training
Text transformer
CSV as input format
Fairness evaluation
Stratified sampling limit
Sliding window
Cutoff Timestamp
Deduplication (remove duplicated features)
Inconsistency (remove features with inconsistent distribution)
Filter (remove low correlations)
Remove missing target rows
Remove category features with unique (only) values
Remove features with constant values
Supported timestamp formats
Forecast window
Lookback window
Number of backtests
Number of prediction columns 1 1 up to 10
Generated experiment notebook
Online deployment
Batch deployment

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AutoAI overview

Parent topic: AutoAI

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