Postmerge fixing Ecology chapter #776
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| @@ -31,7 +31,7 @@ To do so, we will bring together concepts presented in previous chapters and eve | |||
| Fog oases are one of the most fascinating vegetation formations we have ever encountered. | |||
| These formations, locally termed *lomas*, develop on mountains along the coastal deserts of Peru and Chile.^[Similar vegetation formations develop also in other parts of the world, e.g., in Namibia and along the coasts of Yemen and Oman [@galletti_land_2016].] | |||
| The deserts' extreme conditions and remoteness provide the habitat for a unique ecosystem, including species endemic to the fog oases. | |||
| Despite the arid conditions and low levels of precipitation of around 30-50 mm per year on average, fog deposition increases the amount of water available to plants during austal winter. | |||
| Despite the arid conditions and low levels of precipitation of around 30-50 mm per year on average, fog deposition increases the amount of water available to plants during austral winter. | |||
15-eco.Rmd
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| @@ -519,7 +519,7 @@ search_space = paradox::ps( | |||
| Having defined the search space, we are all set for specifying our tuning via the `AutoTuner()` function. | |||
| Since we deal with geographic data, we will again make use of spatial cross-validation to tune the hyperparameters\index{hyperparameter} (see Sections \@ref(intro-cv) and \@ref(spatial-cv-with-mlr)). | |||
| Specifically, we will use a five-fold spatial partitioning with only one repetition (`rsmp()`). | |||
| In each of these spatial partitions, we run 50 models (`trm()`) while using randomly selected hyperparameter configurations (`tnr`) within predefined limits (`seach_space`) to find the optimal hyperparameter\index{hyperparameter} combination. | |||
| In each of these spatial partitions, we run 50 models (`trm()`) while using randomly selected hyperparameter configurations (`tnr()`) within predefined limits (`seach_space`) to find the optimal hyperparameter\index{hyperparameter} combination. | |||
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Definite improvement, follow-on question, worth explaining in more detail what these functions are, I'm new to them and am not sure from this good but terse description.
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Good point, will reference the spatial-cv chapter as I have explained there in a little more detail how to construct an AutoTuner().
15-eco.Rmd
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| @@ -562,12 +562,12 @@ saveRDS(at, "extdata/15-tune.rds") | |||
| ``` | |||
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| ```{r 15-eco-26, echo=FALSE, eval=FALSE} | |||
| tune = readRDS("extdata/15-tune.rds") | |||
| at = readRDS("extdata/15-tune.rds") | |||
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What does at stand for? Autotune? may be worth stating that somewhere or using a longer and more descriptive object name.
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yes, at stands for AutoTuner and I guess this was more obvious while creating the object: https://github.com/Robinlovelace/geocompr/blob/672991f23a7115b77c8ee199bdf8353b84f289f0/15-eco.Rmd#L526-L538
Still, the name could be more descriptive, something like autotuner_rf where rf stands for random forest.
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Still, the name could be more descriptive, something like
autotuner_rfwhere rf stands for random forest.
Agreed, I see now the tests are unrelated to your changes Jannes, so I suggest merging this now to keep the momentum. Many thanks!
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Approved as some definite changes in there. Feel free to merge @jannes-m to keep the momentum going I think 'go fast and break things' is a reasonable attitude. The build seems to be failing with this message: Source: https://github.com/Robinlovelace/geocompr/runs/6077814566?check_suite_focus=true#step:4:5548 |
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give me a few minutes, then I will merge |
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And yes, the momentum is only there because the baby is overdue -:) |
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