Are you looking to build robust, accurate machine-learning models to uncover hidden insights and drive real-world impact? You are in the right place!
XGBoost is widely recognized as one of the most powerful and versatile tools available.
But what sets the XGBoost apart? By deep diving with me into XGBoost, you'll gain that intuition and discover a range of techniques, tools, and libraries to build powerful and effective machine-learning models.
You'll learn how to create optimal models that balance complexity and simplicity. Above and beyond the default settings. You'll gain hands-on experience with real-world data to build models that can generalize well to new data but also uncover hidden insights about your data. Especially useful if you need to uncover bias or deal with regulatory compliance.
With a deep understanding of hyperparameters, early stopping, and hyperopt, you can fine-tune your models to deliver exceptional performance. You'll discover quick tuning techniques that can streamline your workflow. No more wasting time with naive grid search.
I will show you how to determine if you should gather more data and if that will help your model perform better. I also show how to shrink your data so you can deal with only the data that provides a meaningful signal.
You'll gain a deep understanding of model evaluation and metrics to confidently assess your models' performance. You'll discover if a slight change in score matters.
Interpreting the tree model is crucial with black box models like XGBoost. You'll be able to identify complex feature interactions and use SHAP values to gain insight into the importance of each feature in the model's predictions.
In addition, you'll discover how to calibrate your models to better match the actual probabilities of the target variable. By leveraging partial dependence plots (PDP) and individual conditional expectation (ICE) plots, you can visualize the effect of particular features on the model's predictions. You can make simpler models with better performance.
You'll also learn how to apply constraints to your models to ensure they meet specific requirements or adhere to certain rules, such as fairness or ethical considerations.
And when it comes to deploying and serving your models, you'll be well-equipped to handle a range of scenarios, from exporting your models to a production-ready format to deploying them to cloud-based services or embedding them in your applications.