TRAIN OR CLASSIFY USING DECISION TREE¶
Trains a Decision Tree model on a selected dataset column or runs classification using a previously trained tree. Use this worker when interpretable, rule-based classification or regression is needed on tabular data stored in a d3VIEW database.
When to use¶
Tagged: classification, decision_tree, ml, regression, sklearn, supervised_learning, train.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Database | database_id | text | — | ✓ | d3VIEW database reference containing the training dataset; select the target database from the dropdown — required to resolve the feature and label columns. |
| Column Name | train_column | file | — | ✓ | Name of the target (label) column within the selected database that the Decision Tree will learn to predict; must match the column header exactly. |
| Drop Columns | drop_columns | scalar | 1 | Comma-separated list of column names to exclude from training features; default value of 1 means no columns are dropped — override when uninformative or leaky columns need removal. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| Train Model | train_model | text | Serialized Decision Tree model artifact (e.g., pickle/joblib file path or model reference) produced after training; consumed by downstream scoring or evaluation workers. |
Disciplines¶
- ai_ml.model_selection
- ai_ml.supervised.classification
- ai_ml.supervised.regression
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