# XGBoost on SQLFlow Tutorial

This is a tutorial on train/predict XGBoost model in SQLFLow, you can find more SQLFlow usage from the Language Guide, in this tutorial you will learn how to:

- Train a XGBoost model to fit the boston housing dataset; and
- Predict the housing price using the trained model;

## The Dataset

This tutorial would use the Boston Housing as the demonstration dataset. The database contains 506 lines and 14 columns, the meaning of each column is as follows:

Column | Explain |
---|---|

crim | per capita crime rate by town. |

zn | proportion of residential land zoned for lots over 25,000 sq.ft. |

indus | proportion of non-retail business acres per town. |

chas | Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). |

nox | nitrogen oxides concentration (parts per 10 million). |

rm | average number of rooms per dwelling. |

age | proportion of owner-occupied units built prior to 1940. |

dis | weighted mean of distances to five Boston employment centres. |

rad | index of accessibility to radial highways. |

tax | full-value property-tax rate per $10,000. |

ptratio | pupil-teacher ratio by town. |

black | 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town. |

lstat | lower status of the population (percent). |

medv | median value of owner-occupied homes in $1000s. |

We separated the dataset into train/test dataset, which is used to train/predict our model. SQLFlow would automatically split the training dataset into train/validation dataset while training progress.

```
%%sqlflow
describe boston.train;
```

```
%%sqlflow
describe boston.test;
```

## Fit Boston Housing Dataset

First, let’s train an XGBoost regression model to fit the boston housing dataset, we prefer to train the model for `30 rounds`

,
and using `squarederror`

loss function that the SQLFLow extended SQL can be like:

```
TRAIN xgboost.gbtree
WITH
train.num_boost_round=30,
objective="reg:squarederror"
```

`xgboost.gbtree`

is the estimator name, `gbtree`

is one of the XGBoost booster, you can find more information from here.

We can specify the training data columns in `COLUMN clause`

, and the label by `LABEL`

keyword:

```
COLUMN crim, zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, b, lstat
LABEL medv
```

To save the trained model, we can use `INTO clause`

to specify a model name:

```
INTO sqlflow_models.my_xgb_regression_model
```

Second, let’s use a standar SQL to fetch the traning data from table `boston.train`

:

```
SELECT * FROM boston.train
```

Finally, the following is the SQLFlow Train statment of this regression task, you can run it in the cell:

```
%%sqlflow
SELECT * FROM boston.train
TRAIN xgboost.gbtree
WITH
objective="reg:squarederror",
train.num_boost_round = 30
COLUMN crim, zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, b, lstat
LABEL medv
INTO sqlflow_models.my_xgb_regression_model;
```

### Predict the Housing Price

After training the regression model, let’s predict the house price using the trained model.

First, we can specify the trained model by `USING clause`

:

```
USING sqlflow_models.my_xgb_regression_model
```

Than, we can specify the prediction result table by `PREDICT clause`

:

```
PREDICT boston.predict.medv
```

And using a standar SQL to fetch the prediction data:

```
SELECT * FROM boston.test
```

Finally, the following is the SQLFLow Prediction statment:

```
%%sqlflow
SELECT * FROM boston.test
PREDICT boston.predict.medv
USING sqlflow_models.my_xgb_regression_model;
```

Let’s have a glance at prediction results.

```
%%sqlflow
SELECT * FROM boston.predict;
```