When your data is already stored in NumPy arrays, you can directly use it to train an XGBoost model.

Simply pass your feature matrix `X`

and target vector `y`

to the `fit()`

method of your XGBoost model.

```
from xgboost import XGBClassifier
import numpy as np
# Assuming X and y are NumPy arrays
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([0, 1, 1])
# Initialize and train the model
model = XGBClassifier(n_estimators=100, learning_rate=0.1, random_state=42)
model.fit(X, y)
```

Here’s what’s happening:

We assume that our feature matrix

`X`

and target vector`y`

are already stored as NumPy arrays. Remember to ensure that`X`

and`y`

have compatible dimensions before proceeding.We create an instance of the

`XGBClassifier`

(or`XGBRegressor`

for regression tasks) and specify our desired hyperparameters.We directly pass

`X`

and`y`

to the`fit()`

method. XGBoost will use these NumPy arrays during training without any need for conversion.

NumPy arrays are a natural fit for XGBoost, as the library is designed to work efficiently with this data format. By using NumPy arrays, you can avoid the overhead of converting your data to a different format before training your model.