Computation times#
00:00.000 total execution time for 20 files from all galleries:
Example |
Time |
Mem (MB) |
|---|---|---|
Getting Started with skrub ( |
00:00.000 |
0.0 |
Quick overview of DataOps ( |
00:00.000 |
0.0 |
Hands-On with Column Selection and Transformers ( |
00:00.000 |
0.0 |
Deduplicating misspelled categories ( |
00:00.000 |
0.0 |
SquashingScaler: Robust numerical preprocessing for neural networks ( |
00:00.000 |
0.0 |
Sessions in time-based data: Predicting user purchases with the SessionEncoder ( |
00:00.000 |
0.0 |
Encoding: from a dataframe to a numerical matrix for machine learning ( |
00:00.000 |
0.0 |
Various string encoders: a sentiment analysis example ( |
00:00.000 |
0.0 |
Handling datetime features with the DatetimeEncoder ( |
00:00.000 |
0.0 |
Multiples tables: building machine learning pipelines with DataOps ( |
00:00.000 |
0.0 |
Hyperparameter tuning with DataOps ( |
00:00.000 |
0.0 |
Tuning DataOps with Optuna ( |
00:00.000 |
0.0 |
Subsampling for faster development ( |
00:00.000 |
0.0 |
Use case: developing locally and deploying to production ( |
00:00.000 |
0.0 |
Using PyTorch (via skorch) in DataOps ( |
00:00.000 |
0.0 |
Fuzzy joining dirty tables with the Joiner ( |
00:00.000 |
0.0 |
Deduplicating misspelled categories ( |
00:00.000 |
0.0 |
Spatial join for flight data: Joining across multiple columns ( |
00:00.000 |
0.0 |
AggJoiner on a credit fraud dataset ( |
00:00.000 |
0.0 |
Interpolation join: infer missing rows when joining two tables ( |
00:00.000 |
0.0 |