-
大小: 323KB文件类型: .pdf金币: 1下载: 0 次发布日期: 2021-05-08
- 语言: 其他
- 标签: Machine Learning Essentials
资源简介
Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.
This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring data sets, as well as, for building predictive models.
The main parts of the book include:
Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods.
Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies.
Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines.
Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting).
Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables.
Model validation and evaluation techniques for measuring the performance of a predictive model.
Model diagnostics for detecting and fixing a potential problems in a predictive model.
The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.
Key features:
Covers machine learning algorithm and implementation
Key mathematical concepts are presented
Short, self-contained cha
代码片段和文件信息
- 上一篇:SAP GUI 760
- 下一篇:RunHiddenConsole
相关资源
- nndl-book神经网络与深度学习
- Single machine scheduling with processing time
- Physical.Design.Essentials
- Neural Network and Deep Learning中文版
- A Probabilistic Theory of Deep Learning
- 最新迁移学习综述论文A Comprehensive
- Deep Learning for NLP with TensorFlow2.0.zip
- neural networks and deep learning: a textbook
- 迁移学习综述a survey on transfer learnin
- Learning Ceph(2nd) 无水印pdf
- Machine Learning. Algorithms and Applications-
- Distributed Optimization and Statistical Learn
- 英文原版-Machine Learning Using R 1st Edit
- Learning Perl第六版英文版(pdf)
- Learning Perl 7th Edition
-
英文原版-Learning Shell sc
ripting with - Deep Learning with PyTorch
- Learning Rich Features for Image Manipulation
- Learning Pentaho Data Integration 8 CE(3rd)
- ODOO_12_DEVELOPMENT_ESSENTIALS_FOURTH_EDITION
- Introduction.to.Machine.Learning.3rd.Edition
- High Bandwidth Sensorless Algorithm for AC Mac
- rsync-time-backup 使用rsync的Time Machine风格
- Mastering Machine Learning with Scikit-learn -
- Master Machine Learning Algorithms
- Deep Learning in Remote Sensing A Review.pdf
- Optimization Methods for Large-Scale Machine L
- deep learning for anomaly detection a survey(深
- [Perl语言入门(第6版)].(Learning.Pe
- Factored Conditional Restricted Boltzmann Mach
评论
共有 条评论