在R 中使用基于树的模型进行机器学习 - Machine Learning with Tree-Based Models in R 2023-110 0 2025-02-26 13:39:25 您当前的浏览器不支持 HTML5 播放器 请更换浏览器再试试哦~点赞 投币 收藏 分享 稿件举报 记笔记 来自互联网共16P,0.11 GB,有字幕。如需要完整版,请私信我。。
This study aims to assess the bond strength of both plain and deformed steel rebars in recycled aggregate concrete (RAC) using machine learning (ML) methods. The ML models employed include Decision Tree (DT), AdaBoost, CatBoost, Gradient Boosting, and Extreme Gradient Boosting (XGB). A ...
The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care ...
to predict label classification on three different datasets. Here, we'll apply tree-based methods with an eye to see whether we can improve our predictive power on the Santander data used inChapter 3,Logistic Regression, and the data used inChapter 4,Advanced Feature Selection in Linear Models...
The second column, nsplit, is the number of splits in the tree. The rel error column stands for relative error. Both xerror and xstd are based on ten-fold cross-validation, with xerror being the average error and xstd the standard deviation of the cross-validation process. We can see...
4.1ISLR“An Introduction to Statistical Learning with Applications in R”（网站上可以下载书和 R ...
We're going to be using two of the prior datasets, the simulated data from Chapter 4, Advanced Feature Selection in Linear Models, and the customer satisfaction data from Chapter 3, Logistic Regression. We'll start by building a classification tree on the simulated data. This will help us ...
AdaBoost in machine learning is known for its ability to improve the performance of weak learners and create strong models. We will explore the key advantages of AdaBoost simply and understandably. Improved Accuracy:AdaBoost’s primary advantage lies in its ability to enhance the accuracy ofmachine...
The primary challenge in the development of large-scale artificial intelligence (AI) systems lies in achieving scalable decision-making—extending the AI models while maintaining sufficient performance. Existing research indicates that distributed AI can
Ski rental (decision tree) Categorize customers (k-means clustering) 1 - Introduction 2 - Prepare the data 3 - Create the model 4 - Deploy the model NYC taxi tips (classification) Create partition-based models Use SQL ML in R tools ...