Machine learning
Datasets
Use this troubleshooting information when creating and using Datasets.
UNABLE TO LOAD DATA from the Data Lake
Solution: Navigate to the Compass user interface in Data Lake and validate that the query returns data as expected. See the Compass documentation for additional information.
Warning related to MALFORMED ROWS when uploading file
Cause: This could mean that the datatype selected for a field does not conform on some rows or a row has an incorrect number of columns.
Solution: Validate the separated file before upload.
The DATE / TIMESTAMP DATATYPES from the dataset file are not recognized correctly in the metadata section
Solution: When uploading a dataset from file, specify a Date & Timestamp format of the respective dataset in the Details page.
Quests
Use this troubleshooting information when creating and using Quests.
The quest has a FAILED status
Solution: Identify the failing activity and check the error log. A log will be available to the user providing some information to pin point the issue.
The dataset contains MISSING VALUES
Cause: Most algorithms cannot process missing data.
Solution: Check the dataset and handle the missing values by applying the activity before training the model.
The dataset contains features with STRING data type
Cause: Most algorithms cannot process string values.
Solution: Check the dataset and convert the string features into numeric by applying the or activity before training the model.
The LABEL is NOT SPECIFIED before applying a supervised algorithm
Solution: Apply activity and specify the label.
MULTIPLE columns are specified as LABELS
Cause: Only one label can be specified.
Solution: Apply and confirm the label.
The Compare Model activity takes INCOMPARABLE MODELS as inputs
Cause: Models must be of the same type (either classification or regression type) to get comparable results.
Solution: Confirm the model type.
The SCRIPTING activity fails
Solution: Confirm that the output variable is defined in the code as an output of the activity.
Errors in the CONFIGURATION of the ALGORITHM hyperparameters
Solution: Make sure you have these values set correctly:
- XGboost: num.class parameter =! unique number of classes
- XGboost binary: classification objective on multiclass label column
- Linear Learner - multiclass classifier: num.class parameter =! unique number of classes
- Linear Learner - regressor: log not in [0,1] content label column
Training failed with NO ERROR LOG
Cause: For security reasons an error log could not be provided.
Solution: This may be an application error to be resolved.
The error log indicates that a TIMEOUT has occurred on the quest
Solution: Stopping and restarting the quest may resolve the issue.
WHICH ALGORITHM / OBJECTIVE to use
Solution: See the in the main menu.
Long execution times
Use this troubleshooting information when experiencing long execution times.
There is a One-Hot Encoder activity present and running
Cause: The One-Hot Encoder results in too many columns if there is a high number of unique values. This may cause long computation times.
There is a user-defined SCRIPT or SQL activity present and running
Cause: The user has the flexibility to define a script or their own SQL which could result in long running activities.
The DATA volume is LARGE
Cause: Pre-processing and training on large datasets can take time.
Endpoints
Use this troubleshooting information when creating and using Endpoints.
UNABLE TO DEPLOY ENDPOINT - button is disabled
Cause: The production quest must be saved and run with a status value of Finished.
Solution: Save and run the production quest to enable the button.
ENDPOINT TESTING RETURNS AN ERROR
Solution:
- Validate that the endpoint has a status value of Active.
- Validate that the request payload is valid.
- Validate that the request datatypes correspond to the endpoint schema.
- If testing with .csv file input:
- Make sure the .csv file does not contain a header row.
- Validate that the request payload variables come in the same sequence as in the endpoint schema.
- If testing with JSON input:
- Make sure there are no null values in the JSON.
INVOKING the endpoint for PREDICTIONS through API Gateway FAILS
Solution: Validate that the sequence of input values conforms to the schema definition.