ml-flow-random-forest

with mlflow.start_run(run_name="Random_Forest_Undercount"):
    
    from sklearn.ensemble import RandomForestRegressor
    
    # model parameters
    params = {'n_estimators': 1000, 'random_state': 41}

    # log model params
    for key in params:
        mlflow.log_param(key, params[key])

    # train Random Forest model
    RFtree = RandomForestRegressor(**params)
    RFtree.fit(train_features, train_labels)