MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft (by Azure)
feature-engineering-tutorials
Data Science Feature Engineering and Selection Tutorials (by rasgointelligence)
MachineLearningNotebooks | feature-engineering-tutorials | |
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2 | 1 | |
3,958 | 266 | |
0.7% | 1.1% | |
5.6 | 0.0 | |
6 days ago | about 1 month ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
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Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MachineLearningNotebooks
Posts with mentions or reviews of MachineLearningNotebooks.
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Multiple model loading on a Online Fully managed endpoint
I found an example using the python SDK v2:
- I Took The Azure DP-100 exam today and passed it
feature-engineering-tutorials
Posts with mentions or reviews of feature-engineering-tutorials.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-08.
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.