Skip to content

W: XGBoost training (single-dataset)

Brief description

Train a XGBoost classification model on the data.

Parameters

Tag (identifier) (type - string)

Tag to use to help identify object.

Centroid tag (type - string)

Tag to use for the object.

Normalization tag (type - string)

Tag to use for normalization.

Help

Rather than applying a normalization to the entire dataset, we apply it as needed to each task at hand.
You can compare the effect normalization has on specific task by repeating it with different normalization.
In some cases, its advised to use 'multi-dataset' normalization, in particular when doing comparisons.

Normalization name (type - string)

Name of the normalization.

Balance scheme (type - string)

Scheme to use for balancing the data.

Classification type (type - string)

Type of classification to perform.

Positive mask (type - string)

Mask to use for the positive class. If not specified, the mask will be automatically determined.

Negative mask (type - array)

Mask to use for the positive class. If not specified, the mask will be automatically determined.

No. of iterations (type - integer)

Number of training iterations to obtain the best model and estimate SHAP values.

No. of estimators (type - integer)

Number of estimators to use for training.

Max. depth (type - integer)

Maximum depth of the tree.

Subsample (type - number)

Subsample ratio of the training instances. Setting it to 0.8 means that XGBoost would randomly sample 80% of the training data prior to growing trees.

Gamma (type - number)

Minimum loss reduction. Increasing this value will make model more conservative.

Regularization lambda (type - number)

L2 regularization term on weights. Increasing this value will make model more conservative.

Regularization alpha (type - number)

L1 regularization term on weights. Increasing this value will make model more conservative.

Learning rate (type - number)

Step size shrinkage used in update to prevent overfitting.

Colormap (type - string)

Colormap to use for the image.

Plot style (type - array)

Style of the generated figures.

DPI (type - integer)

DPI of the image.

Auto-rotate images (type - boolean)

Automatically rotate images that are taller than they are wider.

Export images as (type - string)

Export figures as image or within a single PDF or PowerPoint.

Export to file (type - array)

Type of output of the supervised data

Attributes

Attribute Value Description
Multiple allowed True Allow multiple instances of this task in a workflow.
Task can fail True Task is optional and can fail without causing the entire workflow to fail.
Step can fail True Sub-tasks of this task can fail without causing the entire task (and workflow) to fail.
Requires ion mobility False Task requires ion mobility data.
Task can fail (with ion mobility) False This task uses ion mobility data but it is allowed to fail, without causing the entire workflow to fail.
Allowed in reference dataset True Task is to be performed on a 'reference' dataset. This will allow for multiple analyses to be performed on the same dataset, without cluttering or duplicating certain tasks (unused at the moment).