While XGBoost is best known for its tree-based models, it also offers a unique feature that allows it to fit linear models. This can be useful for certain types of problems where a linear relationship is expected, or when model interpretability is important.

To configure XGBoost to use a linear model, set the `booster`

parameter to `'gblinear'`

. The `updater`

parameter is then used to specify the linear model algorithm. The available options are `'shotgun'`

for parallel coordinate descent, and `'coord_descent'`

for coordinate descent.

Here’s an example demonstrating how to configure an XGBoost linear model with the `'coord_descent'`

updater for a binary classification task using a synthetic dataset:

```
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score
# Generate a synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize an XGBClassifier with linear booster and coord_descent updater
model = XGBClassifier(booster='gblinear', updater='coord_descent')
# Train the model
model.fit(X_train, y_train)
# Make predictions on the test set
predictions = model.predict(X_test)
# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.4f}")
```

In this example, we first generate a synthetic binary classification dataset using `make_classification()`

from scikit-learn. We then split the data into training and testing sets.

Next, we initialize an `XGBClassifier`

with `booster='gblinear'`

to specify a linear model, and `updater='coord_descent'`

to use the coordinate descent algorithm.

We then train the model using the `fit()`

method, make predictions on the test set using `predict()`

, and evaluate the model’s performance using `accuracy_score()`

.

Using a linear model can be beneficial when you expect a linear relationship between the features and the target, or when you need a more interpretable model. However, for many complex real-world problems, tree-based models often outperform linear models.

As with any model, it’s important to evaluate the performance and compare it with other approaches to determine if a linear model is suitable for your specific problem. If you’re unsure which `updater`

to use, `'coord_descent'`

is generally a good default choice.