Working with time series : denoising and imputation frameworks to improve data density /
"Anjali Samani (CircleUp) explains two simple frameworks for evaluating a dataset's candidacy for smoothing and quantitatively determining the optimal imputation strategy and the number of consecutive missing values that can be imputed without material degradation in signal quality. To ext...
Cote: | Libro Electrónico |
---|---|
Format: | Électronique Vidéo |
Langue: | Inglés |
Publié: |
[Place of publication not identified] :
O'Reilly Media,
2019.
|
Sujets: | |
Accès en ligne: | Texto completo (Requiere registro previo con correo institucional) |
Documents similaires
-
Fast data with the KISSS stack /
Publié: (2019) -
A framework to bootstrap and scale a machine learning function /
Publié: (2019) -
Executive briefing : usable machine learning - lessons from Stanford and beyond /
Publié: (2019) -
See what others can't with spatial analysis and data science /
Publié: (2019) -
Building and managing training datasets for ML with Snorkel /
Publié: (2019)