Model Training with XGBoost#

1. Installation#

To use the Flaml XGBoost AutoML framework, we need to install the extra Flaml dependency in your EvaDB virtual environment.

pip install "flaml[automl]"

2. Example Query#

CREATE FUNCTION IF NOT EXISTS PredictRent FROM
( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
TYPE XGBoost
PREDICT 'rental_price';

In the above query, you are creating a new customized function by training a model from the HomeRentals table using the Flaml XGBoost framework. The rental_price column will be the target column for predication, while the rest columns from the SELECT query are the inputs.

3. Model Training Parameters#

Available Parameters#
PREDICT (required)	The name of the column we wish to predict.
TIME_LIMIT	Time limit to train the model in seconds. Default: 120.
TASK	Specify whether you want to perform `regression` task or `classification` task.
METRIC	Specify the metric that you want to use to train your model. For e.g. for training `regression` tasks you could use the `r2` or `RMSE` metrics. For training `classification` tasks you could use the `accuracy` or `f1_score` metrics.

Below are the example queries specifying the aboe parameters

CREATE FUNCTION IF NOT EXISTS PredictRent FROM
( SELECT number_of_rooms, number_of_bathrooms, days_on_market, rental_price FROM HomeRentals )
TYPE XGBoost
PREDICT 'rental_price'
TIME_LIMIT 180
METRIC 'r2'
TASK 'regression';

CREATE FUNCTION IF NOT EXISTS PredictEmployee FROM
( SELECT payment_tier, age, gender, experience_in_current_domain, leave_or_not FROM Employee )
TYPE XGBoost
PREDICT 'leave_or_not'
TIME_LIMIT 180
METRIC 'accuracy'
TASK 'classification';