Train Regressor — mlTrainRegressor

Fit a final regression model to predict a numeric target.

What it does

Connect training rows to in. The model output is a one-row reference, not the training data or a live Python estimator. It records mlflow_run_id, model_uri, task_type, model_type, target_column, feature_columns_json and model_config_json. Run metadata contains the model URI, run ID and the training metrics described below.

Runtime fits a scikit-learn pipeline, checks its serialized size and saves it to MLflow. If saving fails, the node fails rather than returning a usable model reference. Preview skips fitting and logging and emits a placeholder with null run ID and URI; configuration and schema checks can still fail during preview.

Connect the purple model wire to Predict and evaluate held-out values with Evaluate. Built-in model types are linear_regression, ridge, lasso, random_forest_regressor, svr, xgboost_regressor and lightgbm_regressor. The last two require their optional libraries (pip install "ciaren[ml]"); plugins can add further model types.

Training metadata reports train_r2, train_rmse and train_mae on the training rows. RMSE and MAE use the target's units, and R² can be negative. Use an unfitted Regressor Model with Cross-Validate when you need fold scores.

Configuration

Omitted or empty features select all columns except the target. Selecting the target as a predictor is rejected as leakage. Exclude IDs and unrelated labels explicitly.

Preprocessing and limits

With omitted or empty preprocessing, numeric features use median imputation and standard scaling. Categorical features use most-frequent imputation and one-hot encoding with unknown categories ignored. These steps are fitted on this node's training rows and travel inside the saved pipeline.

For a nonempty preprocessing object, supply numeric_columns and/or categorical_columns: absent lists are empty and columns outside those lists are dropped. If both lists are empty, preprocessing is omitted and the raw selected features reach the estimator. Numeric options are numeric_strategy (standard_scaler, minmax_scaler, robust_scaler, or another value to omit scaling), impute_numeric (default median) and impute_categorical (default most_frequent).

Runtime requires at least ten rows and warns below fifty. Default host limits are 5,000,000 training rows, 500 selected features and 500 MB of serialized model data. Row and feature limits apply before fitting; model size is checked after fitting and before saving. Configure them with CIAREN_ML_MAX_TRAINING_ROWS, CIAREN_ML_MAX_FEATURE_COLUMNS and CIAREN_ML_MAX_MODEL_SIZE_MB. The default hyperparameter numeric bound is 100,000 (CIAREN_ML_MAX_HYPERPARAMETER_VALUE).

MLflow tracking defaults to ./mlruns; set CIAREN_MLFLOW_TRACKING_URI for a configured store. Load only trusted artifacts: MLflow/joblib deserialization can execute Python code, and URI/path checks do not sandbox that code.

Generated Python code

Download the customer sample CSV and save it as customers.csv beside the script. The sample has 16 rows; Train/Test Split uses test_size=0.25 and seed=42 to keep 12 training rows separate from four held-out rows. Install Ciaren with its ML dependencies to run the scripts. Polars exports cross into pandas/scikit-learn for fitting.

These are complete exports from the flow generator. They fit a pipeline in memory; they do not create an MLflow run, save a model artifact, reproduce run metadata metrics or enforce Ciaren's runtime row/feature/model-size guards. The editor's trained-model reference becomes the actual pipeline variable in the script. To retain an exported model, add your own trusted persistence step.

The example fits a 20-tree random forest to predict monthly_charges from tenure and support_calls. The non-stratified split keeps four rows held out. Connect train to Train Regressor, its model wire to Predict, and test to Predict's data input. The output train-regressor.csv contains customers 1, 2, 6, 15 with predictions approximately 82.845, 47.19, 41.505, 82.71. These demonstration results do not establish generalization performance.

The Train configuration is:

{
  "model_type": "random_forest_regressor",
  "target_column": "monthly_charges",
  "feature_columns": [
    "tenure",
    "support_calls"
  ],
  "hyperparameters": {
    "n_estimators": 20
  },
  "seed": 42
}

pandas

import pandas as pd
import joblib
import mlflow.sklearn
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

df_customers = pd.read_csv('customers.csv')
df_1, df_2 = train_test_split(df_customers, test_size=0.25, random_state=42)
df_1 = df_1.reset_index(drop=True)
df_2 = df_2.reset_index(drop=True)
_features = ['tenure', 'support_calls']
_X = df_1[_features]
_numeric = [c for c in _features if pd.api.types.is_numeric_dtype(_X[c])]
_categorical = [c for c in _features if c not in _numeric]
_transformers = []
if _numeric:
    _transformers.append(('num', Pipeline([('impute', SimpleImputer(strategy='median')), ('scale', StandardScaler())]), _numeric))
if _categorical:
    _transformers.append(('cat', Pipeline([('impute', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder(handle_unknown='ignore'))]), _categorical))
_preprocessor = ColumnTransformer(_transformers, remainder='drop')
_y = df_1['monthly_charges']
df_3 = Pipeline([('preprocessor', _preprocessor), ('model', RandomForestRegressor(n_estimators=20, random_state=42))])
df_3.fit(_X, _y)
df_4 = df_2.copy()
_model_features = list(getattr(df_3, 'feature_names_in_', df_2.columns))
df_4['prediction'] = df_3.predict(df_2[_model_features])
df_4.to_csv('train-regressor.csv', index=False)

polars

import polars as pl
import joblib
import mlflow.sklearn
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler

df_customers = pl.read_csv('customers.csv')
_eager_1 = df_customers.to_pandas()
_eager_2, _eager_3 = train_test_split(_eager_1, test_size=0.25, random_state=42)
_eager_2 = _eager_2.reset_index(drop=True)
_eager_3 = _eager_3.reset_index(drop=True)
df_1 = pl.from_pandas(_eager_2)
df_2 = pl.from_pandas(_eager_3)
_eager_4 = df_1.to_pandas()
_features = ['tenure', 'support_calls']
_X = _eager_4[_features]
_numeric = [c for c in _features if pd.api.types.is_numeric_dtype(_X[c])]
_categorical = [c for c in _features if c not in _numeric]
_transformers = []
if _numeric:
    _transformers.append(('num', Pipeline([('impute', SimpleImputer(strategy='median')), ('scale', StandardScaler())]), _numeric))
if _categorical:
    _transformers.append(('cat', Pipeline([('impute', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder(handle_unknown='ignore'))]), _categorical))
_preprocessor = ColumnTransformer(_transformers, remainder='drop')
_y = _eager_4['monthly_charges']
_eager_5 = Pipeline([('preprocessor', _preprocessor), ('model', RandomForestRegressor(n_estimators=20, random_state=42))])
_eager_5.fit(_X, _y)
_eager_6 = df_2.to_pandas()
_eager_7 = _eager_6.copy()
_model_features = list(getattr(_eager_5, 'feature_names_in_', _eager_6.columns))
_eager_7['prediction'] = _eager_5.predict(_eager_6[_model_features])
df_3 = pl.from_pandas(_eager_7)
df_3.write_csv('train-regressor.csv')

Tips and common mistakes

• Keep the target out of selected features, even when it is numeric.
• Train on one branch and score the held-out branch; fitting standalone preprocessing on the full dataset can leak information.
• A supplied seed is still required for estimators without a random-state option, such as Linear Regression. It does not make every algorithm stochastic.
• For negative held-out R², compare against a baseline and inspect features; the value is not necessarily a computation error.

See also

• Predict
• Evaluate
• Regressor Model
• Train/Test Split
• ML quick start