BigBoost tricks refer to a collection of techniques and strategies used to enhance the performance of BigBoost, a popular machine learning algorithm. These tricks can include parameter tuning, feature engineering, or using specific types of data preprocessing. BigBoost is known for its high accuracy, scalability, and speedy performance, making it a favorite among data scientists and machine learning engineers. However, despite its strengths, there are always ways to optimize and enhance its performance. By applying different BigBoost tricks, it is possible to significantly improve the accuracy and speed of your models. These can range from simple techniques such as adjusting the learning rate or changing the number of trees in the model to more complex strategies such as using custom loss functions. Overall, BigBoost tricks serve as a valuable tool for any data scientist or machine learning engineer looking to improve their model's performance.