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### Reason/inspiration (optional)
We would like a new entry on the `Stochastic Gradient Descent` concept under Sklearn. The entry should go in a new file under `docs/content/sklearn/concepts/stochast…
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### Describe the feature or idea you want to propose
Our new experimental forecasting module starts with a really fast ETS implementation. The next stage is AutoETS. But how to search the parameter s…
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I would like to implement stochastic gradient descent from scratch @Anamaya1729
Thank you.
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I would like to suggest adding Optimization functions, machine learning and deep learning algorithms in this repository. This can be added in a separate folder called `machine_learning` folder. Furthe…
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- Implement a linear model which uses stochastic gradient descent to optimize AUC
- See https://takeshimg92.github.io/posts/auc_optimiz.html
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#### Description
Edit SGD to shuffle data before each inner loop
#### Files
* `touvlo/utils.py`
+ Location of SGD implementation
* `tests/test_utils.py`
+ Location of test cases fo…
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In the pdf it looks like the "Mini batch Stochastic Gradient Descent" explanation is repeated from SGD.
sri9s updated
5 years ago
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To implement stochastic gradient descent to optimize a linear regression algorithm on Boston House Prices dataset which is already exists in sklearn as a sklearn.linear_model.SGDRegressor.here,SGD alg…
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Currently, we support batch gradient descent (e.g., LogisticRegression), and stochastic gradient descent (e.g., ``SGDClassifier/SGDRegressor``), but we do not support Mini-Batch gradient descent (``SG…