cranhaven. See also theR-universe documentation.Package: FeatureHashing 0.9.2
Wush Wu
FeatureHashing: Creates a Model Matrix via Feature Hashing with a Formula Interface
Feature hashing, also called as the hashing trick, is a method to transform features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix. Without looking up the indices in an associative array, it applies a hash function to the features and uses their hash values as indices directly. The method of feature hashing in this package was proposed in Weinberger et al. (2009) <arxiv:0902.2206>. The hashing algorithm is the murmurhash3 from the 'digest' package. Please see the README in <https://github.com/wush978/FeatureHashing> for more information.
Authors:
FeatureHashing_0.9.2.tar.gz
FeatureHashing_0.9.2.zip(r-4.5)FeatureHashing_0.9.2.zip(r-4.4)FeatureHashing_0.9.2.zip(r-4.3)
FeatureHashing_0.9.2.tgz(r-4.4-x86_64)FeatureHashing_0.9.2.tgz(r-4.4-arm64)FeatureHashing_0.9.2.tgz(r-4.3-x86_64)FeatureHashing_0.9.2.tgz(r-4.3-arm64)
FeatureHashing_0.9.2.tar.gz(r-4.5-noble)FeatureHashing_0.9.2.tar.gz(r-4.4-noble)
FeatureHashing_0.9.2.tgz(r-4.4-emscripten)FeatureHashing_0.9.2.tgz(r-4.3-emscripten)
FeatureHashing.pdf |FeatureHashing.html✨
FeatureHashing/json (API)
| # Install 'FeatureHashing' in R: |
| install.packages('FeatureHashing', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wush978/featurehashing/issues
- ipinyou.test - IPinYou Real-Time Bidding Dataset for Computational Advertising Research
- ipinyou.train - IPinYou Real-Time Bidding Dataset for Computational Advertising Research
- test.tag - Test.tag
Last updated 16 hours agofrom:ea9524c273 (on package/FeatureHashing). Checks:7 OK, 2 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 19 2025 |
| R-4.5-win-x86_64 | NOTE | Jan 19 2025 |
| R-4.5-linux-x86_64 | NOTE | Jan 19 2025 |
| R-4.4-win-x86_64 | OK | Jan 19 2025 |
| R-4.4-mac-x86_64 | OK | Jan 19 2025 |
| R-4.4-mac-aarch64 | OK | Jan 19 2025 |
| R-4.3-win-x86_64 | OK | Jan 19 2025 |
| R-4.3-mac-x86_64 | OK | Jan 19 2025 |
| R-4.3-mac-aarch64 | OK | Jan 19 2025 |
Exports:hash.mappinghash.signhash.sizehashed.interaction.valuehashed.model.matrixhashed.valueintToRaw
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| CSCMatrix | %*%,CSCMatrix,numeric-method %*%,numeric,CSCMatrix-method CSCMatrix-class dim,CSCMatrix-method dim<-,CSCMatrix-method [,CSCMatrix,missing,numeric,ANY-method [,CSCMatrix,numeric,missing,ANY-method [,CSCMatrix,numeric,numeric,ANY-method |
| Extract mapping between hash and original values | hash.mapping |
| Compute minimum hash size to reduce collision rate | hash.size |
| Create a model matrix with feature hashing | hash.sign hashed.interaction.value hashed.model.matrix hashed.value |
| Convert the integer to raw vector with endian correction | intToRaw |
| iPinYou Real-Time Bidding Dataset for Computational Advertising Research | ipinyou ipinyou.test ipinyou.train |
| Simulate how 'split' work in 'hashed.model.matrix' to split the string into tokens | simulate.split |
| test.tag | test.tag |
