Speeding up experiment training with large data sets

If you find that training a model is timing out or taking an unusually long time, consider these guidelines for reducing training time:

From the Experiment settings pages of the AutoAI tool:

• Make sure that the Optimized algorithm selection option is set to Score and run time.
• Disable the XGBRegressor model. This adjustment can help you obtain results more quickly, but the scores might be slightly lower.

For a coded experiment:

• Pass daub_give_priority_to_runtime parameter as described in the SDK documentation.
  Note: This parameter can increase indeterminism (unreproducibility) of the experiment.

Passing incomplete or outlier input value to deployment can lead to outlier prediction

After you deploy your machine learning model, note that providing input data that is markedly different from data that is used to train the model can produce an outlier prediction. When linear regression algorithms such as Ridge and LinearRegression are passed an out of scale input value, the model extrapolates the values and assigns a relatively large weight to it, producing a score that is not in line with conforming data.

Time Series pipeline with supporting features fails on retrieval

If you train an AutoAI Time Series experiment by using supporting features and you get the error 'Error: name 'tspy_interpolators' is not defined' when the system tries to retrieve the pipeline for predictions, check to make sure your system is running Java 8 or higher.

Running a pipeline or experiment notebook fails with a software specification error

If supported software specifications for AutoAI experiments change, you might get an error when you run a notebook built with an older software specification, such as an older version of Python. In this case, run the experiment again, then save a new notebook and try again.

Resolving an Out of Memory error

If you get a memory error when you run a cell from an AutoAI generated notebook, create a notebook runtime with more resources for the AutoAI notebook and execute the cell again.

Notebook for an experiment with subsampling can fail generating predictions

If you do pipeline refinery to prepare the model, and the experiment uses subsampling of the data during training, you might encounter an “unknown class” error when you run a notebook that is saved from the experiment.

The problem stems from an unknown class that is not included in the training data set. The workaround is to use the entire data set for training or re-create the subsampling that is used in the experiment.

To subsample the training data (before fit()), provide sample size by number of rows or by fraction of the sample (as done in the experiment).

• If number of records was used in subsampling settings, you can increase the value of n. For example:

  train_df = train_df.sample(n=1000)
  

• If subsampling is represented as a fraction of the data set, increase the value of frac. For example:

  train_df = train_df.sample(frac=0.4, random_state=experiment_metadata['random_state'])
  

Pipeline creation fails for binary classification

AutoAI analyzes a subset of the data to determine the best fit for experiment type. If the sample data in the prediction column contains only two values, AutoAI recommends a binary classification experiment and applies the related algorithms. However, if the full data set contains more than two values in the prediction column the binary classification fails and you get an error that indicates that AutoAI cannot create the pipelines.

In this case, manually change the experiment type from binary to either multiclass, for a defined set of values, or regression, for an unspecified set of values.

1. Click the Reconfigure Experiment icon to edit the experiment settings.
2. On the Prediction page of Experiment Settings, change the prediction type to the one that best matches the data in the prediction column.
3. Save the changes and run the experiment again.

Next steps

AutoAI overview

Parent topic: AutoAI overview