Timeline for Hashing Trick - what actually happens
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 12, 2014 at 0:15 | comment | added | cwharland | One Host Encoding isn't a required part of hashing features but is often used alongside since it helps a good bit with predictive power. One way to think of one hot encoding is transforming a feature from a set of N discrete values into a set N binary questions. Perhaps it's not important for me know if feature J is 2 or 3 only that it's not 4. One Hot makes that distinction specific. This helps a lot with linear models whereas ensemble approaches (like RF) will scan break points in the feature to find that distinction. | |
| Oct 12, 2014 at 0:08 | vote | accept | B_Miner | ||
| Oct 12, 2014 at 0:08 | comment | added | B_Miner | So one-hot-encoding is still used, just on hashed values *which as you say saves space and can cause dimensionality reduction (given collisions). Is that correct? | |
| Oct 11, 2014 at 19:48 | history | answered | cwharland | CC BY-SA 3.0 |