Official WeChat account of Tsinghua Big Data Software Team Source: Zhihu This article is about 5000 words long, and it is recommended to read for 5 minutes. This article systematically summarizes the related content about decision trees, random forests, etc. 1、Decision Tree A decision tree is a supervised classification model that essentially selects a feature … Read more
With the continuous development of genetics, breeding, and the increasing advancements in the Human Genome Project and molecular biology, biological data has experienced explosive growth over just a few decades. For example, algorithms such as regression analysis, random forests, and support vector machines in bioinformatics are already quite mature. Recently, while reading literature, I came … Read more
Introduction The previous article introduced the algorithm principles of XGBoost and introduced the scoring function (objective function) that measures the quality of tree structures. The best split point is selected based on the scoring function before and after the feature split points, but a detailed introduction to the node splitting algorithm was not provided. This … Read more
XGBoost and LightGBM are currently very popular tree-based machine learning models, both demonstrating efficient performance. However, they have different characteristics in certain situations. Simple Comparison of XGBoost and LightGBM Training Speed LightGBM has a significant advantage over XGBoost in terms of training speed. This is because LightGBM uses some efficient algorithms and data structures, such … Read more
XGBoost is an excellent algorithm that has been widely used in many competitions such as Kaggle, where we can see that many winning teams utilize XGBoost and achieve outstanding performance. Currently, most classification models in practical applications are based on XGBoost, making it a very practical and user-friendly algorithm. The main reference for this article … Read more
Case Introduction This case aims to predict the Boston housing data using the XGBoost algorithm. The Boston housing dataset is a commonly used dataset for house price prediction, containing 506 samples and 13 features, including crime rates in the area, average number of rooms per dwelling, and distance to the city center. Algorithm Principle XGBoost … Read more
Source: I learned at Xuecheng This article is about 3400 words and is recommended to read in 5 minutes. It evaluates the performance of models from the perspectives of speed and accuracy. Boosting algorithms are a class of machine learning algorithms that build a strong classifier by iteratively training a series of weak classifiers (usually … Read more
Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient implementation of the gradient boosting algorithm. Although other open-source implementations of this method existed before XGBoost, the release of XGBoost seems to have unleashed the power of the technique and brought gradient boosting to the attention of the machine learning community at large. … Read more
Using the XGBoost algorithm often yields good results in Kaggle and other data science competitions, which has made it popular. This article analyzes the prediction process of the XGBoost machine learning model using a specific dataset, and by employing visualization techniques to display the results, we can better understand the model’s prediction process. As the … Read more
In recent years, China has made significant improvements in food quality and safety management. However, with the expansion of the food industry and the increasing demand for inspections, food safety testing data has exhibited high-dimensional, complex, and nonlinear characteristics. These features lead to low data utilization in quantitative analysis, directly affecting the accuracy of risk … Read more