fix doc warnings

CONTRIBUTING.md

@@ -89,7 +89,7 @@ What happens if you accidentally run the bot before the previous run was merged
 If you're stuck or need assistance:
 
 <!-- #TODO #148 - Check our [FAQ](<ADD LINK TO FAQ DOCUMENT>) section first. -->
-* Reach out <!-- on Slack or --> via email for personalised assistance. (See ["Get in touch"](#get-in-touch) above for links.)
+* Reach out <!-- on Slack or --> via email for personalised assistance. (See [Get in touch](#get-in-touch) above for links.)
 * Consider pairing up with a another contributor for guidance. <!-- You can always find us in the Slack channel and we're happy to chat! -->Contact us for guidance on this topic
 
 **Once again, thank you for considering contributing to AutoEmulate! We hope you enjoy your contributing experience.**

docs/reference/emulators/light_gbm.rst

@@ -1,5 +1,5 @@
 autoemulate.emulators.light_gbm
-=============================
+===============================
 
 .. automodule:: autoemulate.emulators.light_gbm
    :members:

docs/tutorials/02_speed.ipynb

@@ -190,7 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1) parallise model fits using `n_jobs`\n",
+    "## 1) parallise model fits using `n_jobs`\n",
     "The n_jobs parameter allows you to specify the number of CPU cores to use for parallel processing. Setting n_jobs = -1  uses all available cores, speeding up computations when working with large datasets.\n",
     "\n",
     "Note: Maxing out all available cores might not always lead to faster computation times. Due to overhead from parallelization, memory bandwidth limitations, and potential load imbalances, using more cores can sometimes result in diminishing returns or even slower performance.\n",
@@ -240,8 +240,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "### 2) restrict the range of models\n",
+    "## 2) restrict the range of models\n",
     "\n",
     "Another approach is to limit the range of models by selecting a subset of relevant types based on your domain and problem expertise. This selection process typically considers factors such as the nature of the problem, data characteristics or the need for interpretability. By narrowing down the types of models, you can reduce the computational burden and focus on the most promising architectures for your specific task."
    ]
@@ -294,7 +293,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3) reduce the number of folds in cross validation using `cross_validator` "
+    "## 3) reduce the number of folds in cross validation using `cross_validator` "
    ]
   },
   {
@@ -348,7 +347,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 4) modify hyperparameter search\n",
+    "## 4) modify hyperparameter search\n",
     "\n",
     "If we want to use hyperparameter search, we suddenly have to fit many more models. For each model, we might have 20 different parameter combinations, and because we cross validate each combination, we are running 20 * 5 = 100 model fits per model. It's therefore recommended to focus on a few models of interest when using hyperparameter search.\n",
     "\n",
@@ -627,7 +626,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,