- Mastering Machine Learning with R
- Cory Lesmeister
- 183字
- 2021-07-09 21:28:18
Modeling
This is where all the work that you've done up to this point can lead to fist-pumping exuberance or fist-pounding exasperation. But hey, if it was that easy, everyone would be doing it. The tasks are as follows:
- Select a modeling technique
- Generate a test design
- Build a model
- Assess a model
Oddly, this process step includes the considerations that you have already thought of and prepared for. In the first step, one will need at least a modicum of an idea about how they will be modeling. Remember, that this is a flexible, iterative process and not some strict linear flowchart such as an aircrew checklist.
The cheat sheet included in this chapter should help guide you in the right direction for the modeling techniques. A test design refers to the creation of your test and train datasets and/or the use of cross-validation and this should have been thought of and accounted for in the data preparation.
Model assessment involves comparing the models with the criteria/criterion that you developed in the business understanding, for example, RMSE, Lift, ROC, and so on.
- SOA實踐
- ASP.NET Core 5.0開發(fā)入門與實戰(zhàn)
- 零基礎玩轉區(qū)塊鏈
- TestNG Beginner's Guide
- Production Ready OpenStack:Recipes for Successful Environments
- Mastering Scientific Computing with R
- C語言程序設計立體化案例教程
- 用戶體驗增長:數(shù)字化·智能化·綠色化
- Extending Puppet(Second Edition)
- 汽車人機交互界面整合設計
- Python程序設計開發(fā)寶典
- 深入實踐DDD:以DSL驅動復雜軟件開發(fā)
- ASP.NET Web API Security Essentials
- Java EE項目應用開發(fā)
- SAS編程演義