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.