Xgboost With Python Gradient Boosted Trees With Xgboost And Scikit Learn Jason Brownlee
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XGBoost With Python
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2016-08-05
XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost. In this Ebook, learn exactly how to get started and bring XGBoost to your own machine learning projects.
Ensemble Learning Algorithms With Python
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2021-04-26
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.
Imbalanced Classification with Python
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2020-01-14
Imbalanced classification are those classification tasks where the distribution of examples across the classes is not equal. Cut through the equations, Greek letters, and confusion, and discover the specialized techniques data preparation techniques, learning algorithms, and performance metrics that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently develop robust models for your own imbalanced classification projects.