Comments (8)
It would make sense to add a subsection on supervised encodings in that chapter.
from fes.
I second the request for a section on likelihood encoding. I've been having a hard time finding a canonical reference on them. I'd also be curious if there are any comparisons between standard likelihood encoding and a multilevel encoding scheme like suggested here.
from fes.
@alexpghayes When I do this for the town variable in the OkC data mentioned in Chapter 5 (glm
= "fixed" and Bayesian glm
as "pooled"), the log-odds estimates are pretty similar. However, the overall sample size isn't small so it would not shrink as much as smaller data sets
from fes.
@topepo ,
Using a multi-level model can make a large difference when the cardinality is large and the data sparse, in my experience. Think about something like zipcodes and the use of logistic regression versus lmer(outcome~1 + (1|zipcode),data=DAT,family=binomial)
to estimate the encodings.
I like the idea of a supervised encoding section. It would be use mentioning transfer learning as well - since this is the starting point for most these days (e.g. NLP)
from fes.
I second the target encoding request. Additional useful references can be found in:
https://arxiv.org/abs/1611.09477
Supervised encoding extends beyond categorical features and one can think of, for example "y-aware dimensionality reduction methods" as a wider definition which would also capture "y-aware PCA"
http://www.win-vector.com/blog/2016/05/pcr_part2_yaware/
from fes.
@topepo Interesting. I'm planning to play around with this some in the next couple of days, I'll let you know if I find a compelling example where pooling makes a big difference.
from fes.
@topepo One example where partial pooling for the encoding can have an impact:
Code (rough, will improve): https://gist.github.com/alexpghayes/d1ffb5c78b2e063d29b415a1a6bcb44e
from fes.
A first pass at some code for recipes is the embed
package that uses stan
or tensorflow
. More documentation is here. Feedback is welcome.
from fes.
Related Issues (20)
- Typo in 1.4 Feature Selection
- Figure 6.5: knots are not plotted HOT 1
- Typo in Intro to Chapter 10
- different output for chapter 11.2 initial ROC analysis HOT 1
- Figure 2.2b plot output in graphic does not match code
- Section 3.4.1 variance reduction not Sqrt(R) HOT 1
- Averaging fold criteria not usually advisable HOT 4
- Leave-one-out cross validation is very valuable HOT 2
- Average {RMSE vs. MSE} over V-folds
- Error in ames_pairwise.R
- Typo in 2.2
- Code missing for results mentioned in Chapter 11.2.1?
- Feature Engineering and Selection: A Practical Approach for Predictive Models (Chapman & Hall/CRC Data Science Series) (English Edition) 1ª Edição, eBook Kindle HOT 1
- Trouble with plotting roc curve(yardstick) in some models HOT 1
- Chapter 6, 9, 10, 12 typos. Version 2019-06-21 HOT 1
- Chapter two - First reference and Table 2.1
- 02_Predicting_Risk_of_Ischemic_Stroke- Coefficients for RFE model HOT 1
- Codes for Section 6.2 are missing HOT 1
- Typo in 2.3
- Typo in preface HOT 2
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 fes.