Comments (2)
In recent Springleaf competition I tested something called margin predictions from xgboost. We tried the following approach.
1 - Made different group of features by intuition.
2 - Ran xgb and predicted CV margin for each set of features.
3 - Used margin as feature when making Ensemble.
You may not have seen importance of some features but lot of times running model on different sets of feature help in Ensemble a lot. My worst model in Springleaf was ExtraTrees but it was the most significant model in Ensemble. Margins created on set of variables which were not so significant also gave good lift in final Ensemble.
from rain-part2.
Thakur, is that like an "offset" in R? It kinda looks like that in the XGBoost documentation, but I can't quite tell.
An offset winds up being something you put in that the model should take into account as something like a linear weight. The model is constrained to use it as-is rather than adjust the coefficient. In a regular linear model, that's almost equivalent to subtracting it from the target, but it's often used in insurance for them to put a number of policy holders into a poisson model, which will then multiply by the other factors to get the predicted number of claims.
We implemented it in H2O and I have tried it often, but have yet to see it be useful for this tactic, though it makes a lot of sense to try.
So....when you use it, you put a prediction vector from some other model into the base_margin parameter? So you pass label and base_margin?
Edit: I don't think I have that right with the explanation of step 2 and 3. I would have thought 2 would be a full model and 3 would be a new model where label is the real targets and base_margin was the output of the model from step 2.
How does ExtraTrees (assuming scikit version) work with step 2?
from rain-part2.
Related Issues (9)
- Getting Started HOT 11
- Our New Team! HOT 11
- Submission Management HOT 78
- Script to produce sample_submission.csv
- Probability distribution matching HOT 2
- Precip-related Functions
- Finding Outliers HOT 4
- Logistic Regression HOT 5
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from rain-part2.