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Hi,
I came across the [paper](https://www.mdpi.com/1099-4300/22/5/545) _Weighted Quantile Regression Forests for Bimodal Distribution Modeling: A Loss Given Default Case_. Basically, the authors p…
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## Feature
In situations when one wants to use R to do a quantile regression, the options available are fairly limited - the `quantreg` package and `quantregForests` are two.
On the other hand,…
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### Describe the workflow you want to enable
Currently, `train_test_split` supports stratified sampling for classification problems using the stratify parameter to ensure that the proportion of cla…
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Add support for quantile linear regression, e.g.
```{r}
library(parsnip)
linear_reg() %>%
set_engine("quantreg", tau = 0.50) %>%
set_mode("quantiles") # maybe unnecessary... see #85
``…
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We are adding a mode for quantile regression but have one engine that already enables such prediction (using the censored regression mode).
We should allow that but make some adjustments to harmon…
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followup to #696
specifically what inference can we use after estimation.
question that I'm looking at right now is whether QuantReg should use t or normal distribution. (I'm adding a `use_t` option …
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One of the top 4 papers https://arxiv.org/pdf/1809.03561.pdf in Gefcom 2017 used this approach. It's relatively simple compared to some other methods, so would be worth trying on this dataset.
Cal…
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Hello!
I was wondering if there is any native way of doing quantile regression with Flaml or any quick way of going about setting it up so that it works with it. I was taking a look at the tutorial…
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I am using quantile forest in the GRF package on my data which is around 12 million records. I have a couple of questions:
- The model trains fine, ie, in around 1 hour when i use 300 trees (am run…
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## Summary
Hello, while using LightGBM to estimate multiple quantiles, I have encountered several issues where the monotonicity between quantiles is not guaranteed just as https://github.com/microsof…