Comments (6)
Both things are on my TODO list for a while:
- Fix error for
respect.unordered.factors = "order"
- How to handle new levels in general?
On 2.: In randomForest
all new factor levels go to the left. Currently the same is done in ranger
. However, we should change this. Any better ideas than just assign them randomly?
from ranger.
Oh I see. So I guess the same strategy (always to the left) is also used when calculating the OOB predictions. Maybe you can leave it like that until we have strategies of handling missing values in predictors? Then you could start to offer an option like newFactorLevels = c("left", "right", "missing")
or so.
from ranger.
Error fixed in #120.
from ranger.
In randomForest all new factor levels go to the left.
i'm not sure about this
randomForest.predict returns error "new factor levels not present in the training data" when it is faced with unseen levels.
from ranger.
Yes, you are right. I was checking for factor levels being present in the levels but not in the data. However, I've just checked that again and it was changed in a recent version of randomForest
: They are assigned randomly since version 4.6-10.
from ranger.
from ranger.
Related Issues (20)
- Define a custom loss function HOT 2
- R squared in stand alone version
- Error: Missing data in columns: predictor1, predictor2.... HOT 2
- Incorrect bounds check in beta log probability HOT 2
- Different predictions based on variable importance setting HOT 4
- `ranger()` finished, but `rsession` is still running HOT 8
- Trying to spatially predict(type="se")$se and the output has many null values and what look like spatial offsets? HOT 6
- compilation failed for package ‘ranger’ HOT 34
- ranger-cli fails to build HOT 1
- treeInfo() fails for probability trees with non-factor response HOT 1
- Poor handling of character string predictors HOT 6
- Increasing mtry crashes ranger fit HOT 3
- make fails => cannot compile C++ source on Mac HOT 2
- Error updating the package HOT 14
- warnings generated running 'Understanding random forests with randomForestExplainer' code HOT 1
- num.threads causing crashes inside caret recursive feature elimination wrapper HOT 1
- Results from importance_pvalues() differ despite setting seed HOT 1
- Decision Tree Build HOT 2
- Random forest prediction intervals using the out-of-bag predictions errors. HOT 2
- Is there a way to fit an isolation forest using ranger? HOT 1
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 ranger.