- Statistics for Machine Learning
- Pratap Dangeti
- 320字
- 2021-07-02 19:05:58
Compensating factors in machine learning models
Compensating factors in machine learning models to equate statistical diagnostics is explained with the example of a beam being supported by two supports. If one of the supports doesn't exist, the beam will eventually fall down by moving out of balance. A similar analogy is applied for comparing statistical modeling and machine learning methodologies here.
The two-point validation is performed on the statistical modeling methodology on training data using overall model accuracy and individual parameters significance test. Due to the fact that either linear or logistic regression has less variance by shape of the model itself, hence there would be very little chance of it working worse on unseen data. Hence, during deployment, these models do not incur too many deviated results.
However, in the machine learning space, models have a high degree of flexibility which can change from simple to highly complex. On top, statistical diagnostics on individual variables are not performed in machine learning. Hence, it is important to ensure the robustness to avoid overfitting of the models, which will ensure its usability during the implementation phase to ensure correct usage on unseen data.
As mentioned previously, in machine learning, data will be split into three parts (train data - 50 percent, validation data - 25 percent, testing data - 25 percent) rather than two parts in statistical methodology. Machine learning models should be developed on training data, and its hyperparameters should be tuned based on validation data to ensure the two-point validation equivalence; this way, the robustness of models is ensured without diagnostics performed at an individual variable level:

Before diving deep into comparisons between both streams, we will start understanding the fundamentals of each model individually. Let us start with linear regression! This model might sound trivial; however, knowing the linear regression working principles will create a foundation for more advanced statistical and machine learning models. Below are the assumptions of linear regression.
- 深入核心的敏捷開發:ThoughtWorks五大關鍵實踐
- Mastering RabbitMQ
- C#程序設計教程
- Python程序設計
- Hands-On GPU:Accelerated Computer Vision with OpenCV and CUDA
- C#實踐教程(第2版)
- Visual FoxPro程序設計習題集及實驗指導(第四版)
- Java程序設計入門
- MySQL入門很輕松(微課超值版)
- Learning Modular Java Programming
- 快速入門與進階:Creo 4·0全實例精講
- Spring Boot+MVC實戰指南
- Java Web從入門到精通(第2版)
- Python Projects for Kids
- 軟件測試技術