A grab bag of assorted Data science tools from Moz.
Currently includes:
Utilities for training/evaluating machine learning models:
Cross validation
Evaluation metrics (AUC, F1, etc)
Training models in parallel
Ensemble model selection
PCA
A generic way to specify model inputs
Some linear models:
Linear Regression
Logistic Regression
GLM
Installing
pip install mozsci
1 maintainer
Metadata
1 maintainer
Release files for mozsci 0.9.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| mozsci-0.9.2.tar.gz | 34.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mozsci-0.9.2-cp27-none-macosx_10_10_intel.whl | CPython 2.7 | none | macOS 10.10+ Intel (x86-64, i386) | Details |
Total release size: 114.9 kB
Release files / mozsci-0.9.2.tar.gz
| Download URL | mozsci-0.9.2.tar.gz |
|---|---|
| Size | 34.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
eb7fda57c357e503e82f7ed9634b1c406fef660c3dac07c31dadfd64234a66c2
|
|
BLAKE2b-256 checksum How to use checksums |
70093d2ddb739aadeb93a04957f7e26c0405ff040020517e18181893e3c87628
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Release files / mozsci-0.9.2-cp27-none-macosx_10_10_intel.whl
| Download URL | mozsci-0.9.2-cp27-none-macosx_10_10_intel.whl |
|---|---|
| Size | 80.2 kB |
| Tags | CPython 2.7 macOS 10.10+ Intel (x86-64, i386) |
|
SHA-256 checksum How to use checksums |
2ee766a47ee300cee198a0e625890db30bd8d831519df74ec4613dd8b0bd9a4f
|
|
BLAKE2b-256 checksum How to use checksums |
9a865f29a929cb5acb95a5d74ad0d089100effa5c9d40c5287ea85c0418239d2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for mozsci 0.9.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
