gavindiaz/V20pyPro
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Peter Newell
2018-05-22 21:02:36 +08:00
committed by GitHub
parent 9115df2653
commit 948ad90df3
1 changed files with 2 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+2
View File
@@ -4,6 +4,8 @@ This project provides several examples of common machine learning models applied
Ideally, this project would help make these tools more accessible for those learning to apply machine learning to financial markets. Ideally, this project would help make these tools more accessible for those learning to apply machine learning to financial markets.
*TODO: showcase quant functions and extend database usage examples.*
## Ensemble Research ## Ensemble Research
Using a basic list of six standard Sci-kit Learn ensemble methods, we can explore the effectiveness of these off-the-shelf models. Out of the box, these models can achieve 65-75% accuracy but could be improved by manipulating learning rates, increasing the number of estimators, or for some models, including a base estimator; i.e. another predictive model that improves the Booster's reliability. Using a basic list of six standard Sci-kit Learn ensemble methods, we can explore the effectiveness of these off-the-shelf models. Out of the box, these models can achieve 65-75% accuracy but could be improved by manipulating learning rates, increasing the number of estimators, or for some models, including a base estimator; i.e. another predictive model that improves the Booster's reliability.