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Engine docs for censored (#753)
* add details on time predictions for censored * udpated `.md` files for censored-related models/enginges * use templates * engine doc refresh * fix broken h2o links in docs Co-authored-by: Max Kuhn <[email protected]>
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man/details_linear_reg_h2o.Rd

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man/details_logistic_reg_h2o.Rd

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man/details_multinom_reg_h2o.Rd

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man/details_poisson_reg_h2o.Rd

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man/rmd/bag_tree_rpart.Rmd

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```{r child = "template-uses-case-weights.Rmd"}
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```
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## Other details
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```{r child = "template-survival-median.Rmd"}
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```
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## References
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- Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/bag_tree_rpart.md

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The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that expect vectors of case weights.
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## Other details
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Predictions of type `"time"` are predictions of the median survival time.
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## References
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- Breiman L. 1996. "Bagging predictors". Machine Learning. 24 (2): 123-140

man/rmd/boost_tree_mboost.Rmd

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```{r child = "template-tree-split-factors.Rmd"}
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```
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## Other details
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```{r child = "template-survival-mean.Rmd"}
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```
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## References
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- Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. _Statistical Science_, 22(4), 477–505.

man/rmd/boost_tree_mboost.md

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This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. `{a, c}` vs `{b, d}`) when splitting at a node. Dummy variables are not required for this model.
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## Other details
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Predictions of type `"time"` are predictions of the mean survival time.
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## References
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- Buehlmann P, Hothorn T. 2007. Boosting algorithms: regularization, prediction and model fitting. _Statistical Science_, 22(4), 477–505.

man/rmd/decision_tree_partykit.Rmd

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```{r child = "template-tree-split-factors.Rmd"}
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```
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## Other details
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```{r child = "template-survival-median.Rmd"}
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```
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## References
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- [partykit: A Modular Toolkit for Recursive Partytioning in R](https://jmlr.org/papers/v16/hothorn15a.html)

man/rmd/decision_tree_partykit.md

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This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. `{a, c}` vs `{b, d}`) when splitting at a node. Dummy variables are not required for this model.
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## Other details
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Predictions of type `"time"` are predictions of the median survival time.
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## References
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- [partykit: A Modular Toolkit for Recursive Partytioning in R](https://jmlr.org/papers/v16/hothorn15a.html)

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