Some improvements to OOB notebook

.gitignore

@@ -139,6 +139,7 @@ pylint.html
 # Saved data
 runs/
 data/models/
+*.pkl
 
 # Docs
 docs_build

CONTRIBUTING.md

@@ -151,7 +151,7 @@ cells which are then hidden in the documentation.
 
 In order to do this, cells are marked with tags understood by the mkdocs
 plugin [`mkdocs-jupyter`](https://github.com/danielfrg/mkdocs-jupyter#readme),
-namely adding the following to the relevant cells:
+namely adding the following to the metadata of the relevant cells:
 
 ```yaml
 "tags": [

docs/css/extra.css

@@ -49,7 +49,12 @@ a.autorefs-external:hover::after {
 }
 
 .md-typeset h2 {
-  font-size: 1.7em;
+  font-size: 1.3em;
+  font-weight: 300;
+}
+
+.md-typeset h3 {
+  font-size: 1.1em;
   font-weight: 300;
 }
 
@@ -77,12 +82,6 @@ a.autorefs-external:hover::after {
     user-select: none;
 }
 
-/* Nicer style of headers in generated API */
-h2 code {
-    font-size: large!important;
-    background-color: inherit!important;
-}
-
 /* Remove cell input and output prompt */
 .jp-InputArea-prompt, .jp-OutputArea-prompt {
     display: none !important;

docs/value/index.md

@@ -15,8 +15,8 @@ alias:
 training set which reflects its contribution to the final performance of some
 model trained on it. Some methods attempt to be model-agnostic, but in most
 cases the model is an integral part of the method. In these cases, this number
-not an intrinsic property of the element of interest, but typically a function
-of three factors:
+is not an intrinsic property of the element of interest, but typically a
+function of three factors:
 
 1. The dataset $D$, or more generally, the distribution it was sampled
    from (with this we mean that *value* would ideally be the (expected)
@@ -28,8 +28,8 @@ of three factors:
    of a linear model.
 
 3. The performance metric of interest $u$ for the problem. When value depends on
-   a model, it must be measured in some way which uses it. E.g. the $R^2$ score or
-   the negative MSE over a test set.
+   a model, it must be measured in some way which uses it. E.g. the $R^2$ score
+   or the negative MSE over a test set.
 
 pyDVL collects algorithms for the computation of data values in this sense,
 mostly those derived from cooperative game theory. The methods can be found in