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## Description
For a random forest model contributions are not averaged across individual trees.
Below you can see that the contributions (plus expectation) sum to the raw prediction (sum of predi…
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H2o should support running Multivariate regression, i.e. multiple Y continuous real-valued output response variables (NOT autoencoders), using algorithms i.e. DLNN, GBM, Random Forests.
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In [PySpark Integration Test](https://github.com/ucbrise/clipper/blob/09dfc9766e9f5bebefe6cdc7690267d2860028f4/integration-tests/deploy_pyspark_models.py#L162-L178) we have the following lines:
```…
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I'm setting up an issue for the random forest template that @Gitiauxx is working on! Tagging @waddell and @Arezoo-bz for feedback and additional guidance on use cases for the template.
### Goals
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- [ ] [test_fil.py::test_fil_regression[11-90-1000-500000]](https://github.com/rapidsai/cuml/issues/2920)
- [ ] [test_nearest_neighbors.py::test_knn_separate_index_search](https://github.com/rapidsai…
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Real-time prediction of stock market trends based on news, tweets,
and historical price. A supervised machine learning algorithm such as SVM, Random Forest, Logistic Regression can be used.
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We have very high dimensional data and we need to build many models in a binary representation. To tackle this, we have to take the help of Logistic Regression with One vs Rest classifier. The classif…
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# Main Remark
Tabnet architecture is using sequential steps in order to mimic some kind of random forest paradigm.
But since boosting algorithms often outperform random forests shouldn't we try to…
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## Summary
Be able to train a model using the following workflow:
- select goal
- select model to use
- they cannot use a regression model for a classification problem
- process data for model
…