# Tuning and scaling XGBClassifier

In this section, we will fine-tune and scale XGBClassifier to obtain the best possible `recall_score`

value for the Exoplanets dataset. First, you will adjust weights using `scale_pos_weight`

, then you will run grid searches to find the best combination of hyperparameters. In addition, you will score models for different subsets of the data before consolidating and analyzing the results.

## Adjusting weights

In *Chapter 5*, *XGBoost Unveiled*, you used the `scale_pos_weight`

hyperparameter to counteract imbalances in the Higgs boson dataset. `Scale_pos_weight`

is a hyperparameter used to scale the *positive* weight. The emphasis here on *positive* is important because XGBoost assumes that a target value of `1`

is *positive* and a target value of `0`

is *negative*.

In the Exoplanet dataset, we have been using the default `1`

as negative and `2`

as positive as provided by the dataset. We will now switch to `0`

as negative and `1`

as positive using the `.replace()`

method.