- Statistics for Machine Learning
- Pratap Dangeti
- 159字
- 2021-07-02 19:06:00
Summary
In this chapter, you have learned the comparison of statistical models with machine learning models applied on regression problems. The multiple linear regression methodology has been illustrated with a step-by-step iterative process using the statsmodel package by removing insignificant and multi-collinear variables. Whereas, in machine learning models, removal of variables does not need to be removed and weights get adjusted automatically, but have parameters which can be tuned to fine-tune the model fit, as machine learning models learn by themselves based on data rather than exclusively being modeled by removing variables manually. Though we got almost the same accuracy results between linear regression and lasso/ridge regression methodologies, by using highly powerful machine learning models such as random forest, we can achieve much better uplift in model accuracy than conventional statistical models. In the next chapter, we will be covering a classification example with logistic regression and a highly powerful machine learning model, such as random forest, in detail.
- 軟件安全技術(shù)
- C程序設(shè)計(jì)簡明教程(第二版)
- C語言程序設(shè)計(jì)習(xí)題解析與上機(jī)指導(dǎo)(第4版)
- Essential Angular
- React.js Essentials
- Instant RubyMotion App Development
- Mastering Android Development with Kotlin
- 開源項(xiàng)目成功之道
- FPGA嵌入式項(xiàng)目開發(fā)實(shí)戰(zhàn)
- Instant jQuery Boilerplate for Plugins
- Natural Language Processing with Python Quick Start Guide
- Unity Android Game Development by Example Beginner's Guide
- NGUI for Unity
- Mastering Android Studio 3
- C語言從入門到精通(第5版)