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XGBoost for Learn to Rank

Learning to rank is a crucial task in information retrieval systems like search engines, recommendation systems, and online advertising.

XGBoost, with its powerful gradient boosting algorithm, is well-suited for building ranking models.

Here’s a quick example of how you can use XGBoost’s native API to train a ranking model on a synthetic dataset.

# XGBoosting.com
# XGBoost for Learn to Rank
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import numpy as np
import xgboost as xgb

# Generate a synthetic dataset for ranking
X, y = make_classification(n_samples=1000, n_classes=10, n_informative=5, n_clusters_per_class=1, random_state=42)

# Split into train and test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert data into DMatrix format
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# Define XGBoost parameters
params = {'objective': 'rank:pairwise',
    'learning_rate': 0.1,
    'gamma': 1.0,
    'min_child_weight': 0.1,
    'max_depth': 6}
num_rounds = 100

# Train the model
model = xgb.train(params, dtrain, num_boost_round=num_rounds)

# Make predictions on the test set
preds = model.predict(dtest)
print("Predicted rankings:", preds[:5])

To build a ranking model with XGBoost:

  1. Prepare your data with relevant features for ranking. Here, we generate a synthetic dataset using NumPy.
  2. Convert your data into XGBoost’s DMatrix format. For ranking, you need to specify the group parameter to indicate the groups within which to perform ranking.
  3. Define your XGBoost parameters. Importantly, set the objective to rank:pairwise for pairwise ranking.
  4. Train the model using xgb.train().
  5. Use the trained model to generate rankings on a test set.

With this, you have a working XGBoost ranking model that you can apply to your real-world ranking problems.

See Also