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Configure XGBoost "rank:ndcg" Objective

Learning to rank with XGBoost’s “rank:ndcg” objective is pivotal in applications where the order of items based on relevance is crucial, such as search engines and recommendation systems.

This guide demonstrates setting up XGBoost to optimize ranking tasks, ensuring that the items are sorted according to their actual relevance or usefulness.

import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Generate a synthetic dataset for ranking
X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_clusters_per_class=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Group data points to form queries in a ranking context
# Ensuring the sum of groups equals the number of training data points (80% of 1000 here)
group_sizes = [160, 140, 140, 120, 120, 120]

# Convert data into DMatrix, specifying group information for ranking
dtrain = xgb.DMatrix(X_train, label=y_train)

# Define parameters for the model using the 'rank:ndcg' objective
params = {
    'objective': 'rank:ndcg',
    'learning_rate': 0.1,
    'gamma': 0.1,
    'min_child_weight': 0.1,
    'max_depth': 6

# Train the model
bst = xgb.train(params, dtrain, num_boost_round=100)

# Make predictions on the test set
predictions = bst.predict(xgb.DMatrix(X_test))
print("Predicted rankings:", predictions)

When configuring the “rank:ndcg” objective in XGBoost, it is essential to consider the specific characteristics of your data and the ranking task at hand. Here are some tips to enhance your model’s performance:

  1. Parameter Tuning: Adjust the gamma, min_child_weight, and max_depth parameters to manage the complexity of the model and prevent overfitting. These parameters are influential in how the model handles the ranking of items within each group.
  2. Effective Grouping: The way you group data points can significantly affect the learning outcome. Ensure your groups correctly represent the queries or user sessions for accurate relevance learning.
  3. Evaluation Metrics: Utilize appropriate ranking metrics such as Normalized Discounted Cumulative Gain (NDCG), Mean Reciprocal Rank (MRR), or Average Precision (AP) to evaluate your model. These metrics help in understanding how well your model is performing in predicting the correct order of items.

See Also