fixreadme

eco3 · Jun 14, 2021 · e58d0b5 · e58d0b5
1 parent 250ffb7
commit e58d0b5
Show file tree

Hide file tree

Showing 3 changed files with 5 additions and 6 deletions.
diff --git a/examples/README.md b/examples/README.md
@@ -1,7 +1,7 @@
 # Examples #
 
-* For examples using the PyTorch backend, see the [pytorch](https://github.com/elephaint/pgbm/blob/main/examples/pytorch/) folder.
-* For examples using the Numba backend, see the [pytorch](https://github.com/elephaint/pgbm/blob/main/examples/pytorch/) folder. The Numba backend does not support autodifferentiation and it supports less distributions.
+* For examples using the PyTorch backend (`pgbm`), see the [pytorch](https://github.com/elephaint/pgbm/blob/main/examples/pytorch/) folder.
+* For examples using the Numba backend (`pgbm_nb`), see the [numba](https://github.com/elephaint/pgbm/blob/main/examples/numba/) folder. The Numba backend does not support autodifferentiation and it supports less distributions.
 
 # Hyperparameters #
 PGBM employs the following set of hyperparameters (listed in alphabetical order):

diff --git a/examples/numba/README.md b/examples/numba/README.md
@@ -6,14 +6,13 @@ This folder contains examples of PGBM. The examples illustrate the following:
 * Examples 5-6: How PGBM compares to other methods such as NGBoost and LightGBM.
 * Example 7: How the choice of output distribution can be optimized after training.
 * Example 9: How to plot the feature importance of a learner after training.
-* Example 10: How we employed PGBM to forecast Covid-19 daily hospital admissions in the Netherlands.
 * Example 11: How to save and load a PGBM model. 
 
 Note: to use the `higgs` dataset in any of the examples, download [here](https://archive.ics.uci.edu/ml/datasets/HIGGS), unpack and save `HIGGS.csv` to your local working directory.
 
-Below is an example of a probabilistic regression task: predict housing prices for the [Boston Housing dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/). The code for this example can be found [here](https://github.com/elephaint/pgbm/blob/main/examples/example1_bostonhousing.py).
+Below is an example of a probabilistic regression task: predict housing prices for the [Boston Housing dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/). The code for this example can be found [here](https://github.com/elephaint/pgbm/blob/main/examples/numba/example01_bostonhousing_cpu.py).
 
-First, we import the necessary packages. In this simple example we will train on the CPU.
+First, we import the necessary packages.
 ```
 from pgbm_nb import PGBM
 import numpy as np

diff --git a/examples/pytorch/README.md b/examples/pytorch/README.md
@@ -12,7 +12,7 @@ This folder contains examples of PGBM. The examples illustrate the following:
 
 Note: to use the `higgs` dataset in any of the examples, download [here](https://archive.ics.uci.edu/ml/datasets/HIGGS), unpack and save `HIGGS.csv` to your local working directory.
 
-Below is an example of a probabilistic regression task: predict housing prices for the [Boston Housing dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/). The code for this example can be found [here](https://github.com/elephaint/pgbm/blob/main/examples/example1_bostonhousing.py).
+Below is an example of a probabilistic regression task: predict housing prices for the [Boston Housing dataset](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/). The code for this example can be found [here](https://github.com/elephaint/pgbm/blob/main/examples/pytorch/example01_bostonhousing_cpu.py).
 
 First, we import the necessary packages. In this simple example we will train on the CPU.
 ```