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ML: Train Model

Description

Section titled “Description”

The ML: Train Model step fits a scikit-learn model on a source table and writes the result as a one-row model table. The model table flows through the workflow like any other table — pass it to an ML: Score step to append predictions to a data table, or query it directly to inspect the training metrics. The step runs as a managed PlaidCloud job (like the LLM step), so training does not compete with the rest of the workflow for resources.

Model Tab

Section titled “Model Tab”

Source and Output

Section titled “Source and Output”
  • Training Table — the table to train on. Rows with an empty target value are excluded from training.
  • Model Table — the output table that receives the one-row model record.

Model

Section titled “Model”

  • Algorithm — the model to fit:

    Algorithm Value Family
    Logistic Regression logistic_regression Classification
    Decision Tree Classifier decision_tree Classification
    Random Forest Classifier random_forest Classification
    Gradient Boosting Classifier (XGBoost-style) xgb_classifier Classification
    Linear Regression linear_regression Regression
    Decision Tree Regressor decision_tree_regressor Regression
    Random Forest Regressor random_forest_regressor Regression

    The XGBoost-style algorithm is a scikit-learn HistGradientBoostingClassifier — a statistically equivalent stand-in for XGBoost, not a wrapper around it. See Limits and Caveats.

  • Target Column — the column to predict. Classification targets are treated as text labels; regression targets are coerced to numbers, and rows whose target does not parse as a number are excluded.

Hyperparameters

Section titled “Hyperparameters”

An optional JSON object of scikit-learn hyperparameters for the chosen algorithm — for example {"max_depth": 5, "n_estimators": 200}. Unknown keys are rejected when you save the step, so a mistyped parameter fails immediately instead of being silently ignored. Workflows converted from Alteryx may also carry Alteryx-style sentinel keys such as max_depth_none; these are translated for you.

A few legacy parameters that scikit-learn has removed are accepted but not applied (for example presort, or the XGBoost tree knobs listed under Limits and Caveats); anything dropped this way is recorded in the model table’s params_json under dropped_params, so nothing disappears silently.

Features Tab

Section titled “Features Tab”

The Features tab shows one row per source-table column:

  • Feature — check the columns to train on. Leave all boxes unchecked to train on every column except the target.
  • Type Override — force a column to Numeric or Categorical handling, or leave it at (infer) to derive the type from the table column type.

Feature preparation matches Alteryx Assisted Modeling: numeric features are coerced to numbers (unparseable values become missing) and missing values are imputed with the median; categorical features are treated as text, imputed with the most frequent value, and one-hot encoded. Values a categorical feature never saw during training are ignored at scoring time.

The Model Table

Section titled “The Model Table”

The step writes exactly one row with these columns:

Column Contents
model_pickle_b64 The fitted scikit-learn pipeline (pickled, base64-encoded).
algorithm The algorithm value, for example logistic_regression.
params_json JSON: the applied scikit-learn parameters, the feature types used, and any dropped_params.
feature_names_json JSON array: the ordered feature columns the model expects at scoring time.
class_labels_json JSON array of class labels (classifiers) or null (regressors).
metrics_json JSON training metrics — see below.
trained_at UTC timestamp of the training run.

Because the model is an ordinary table, you can query it like one — for example, extract metrics_json in a downstream step to gate a workflow on model quality.

metrics_json contains a kind key (classification or regression), train_accuracy for classifiers or train_r2 and train_rmse for regressors, and n_training_rows.

Limits and Caveats

Section titled “Limits and Caveats”
  • Training metrics are computed on the training set — there is no holdout split or cross-validation in this release. Treat them as a fit check, not an estimate of out-of-sample performance.
  • In-memory working set — the training table is read into memory, so the step targets roughly one million rows. Sample or aggregate larger tables first.
  • XGBoost approximation — xgb_classifier trains a scikit-learn HistGradientBoostingClassifier. The XGBoost-specific tree parameters gamma, min_child_weight, subsample, and the colsample_* family have no equivalent and are dropped (recorded in dropped_params, and in the step’s mapping notes when the step was converted from Alteryx). Validate model metrics against your reference run before relying on parity.
Section titled “Related”