diff --git a/CHANGELOG.md b/CHANGELOG.md
index c4f9c89b..2dee46ba 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,49 +1,17 @@
 # Change Log
 
-From v3.0.2 to v3.1.0
+From v3.1.1 to v3.1.2
 
 ## Fixes
 
-- Fixed a bug in `QSPRDataset` where property transformations were not applied.
-- Fixed a bug where an attached standardizer would be refit when calling
-  `QSPRModel.predictMols` with `use_applicability_domain=True`.
-- Fixed random seed not set in `FoldsFromDataSplit.iterFolds` for `ClusterSplit`.
 
 ## Changes
 
-- renamed `PandasDataTable.transform` to `PandasDataTable.transformProperties`
-- moved `imputeProperties`, `dropEmptyProperties` and `hasProperty` from `MoleculeTable`
-  to `PandasDataTable`.
-- removed `getProperties`, `addProperty`, `removeProperty`, now use `PandasDataTable`
-  methods directly.
-- Since the way descriptors are saved has changed, this release is incompatible with
-  previous data sets and models. However, these can be easily converted to the new
-  format by adding
-  a prefix with descriptor set name to the old descriptor tables. Feel free to contact
-  us if you require assistance with this.
-- Due to some changes in `rdkit-2023.9.6`, the `add_rdkit`
-  option for molecule tables temporarily might not work.
-  This also affects the current ChemProp integration, which was not adapted to 2.0.0 yet.  
-  In order to prevent these issues, QSPRpred now forces rdkit version `rdkit-2023.9.5`, 
-  but we will be working on resolving these.
 
 ## New Features
 
-- Descriptors are now saved with prefixes to indicate the descriptor sets. This reduces
-  the chance of name collisions when using multiple descriptor sets.
-- Added new methods to `MoleculeTable` and `QSARDataset` for more fine-grained control
-  of clearing, dropping and restoring of descriptor sets calculated for the dataset.
-    - `dropDescriptorSets` will drop descriptors associated with the given descriptor
-      sets.
-    - `dropDescriptors` will drop individual descriptors associated with the given
-      descriptor sets and properties.
-    - All drop actions are restorable with `restoreDescriptorSets` unless explicitly
-      cleared from the data set with the `clear` parameter of `dropDescriptorSets`.
-- Added a proper API for parallelization backend selection and configuration (see
-  documentation of `ParallelGenerator` and `JITParallelGenerator` for more information).
-- Clusters can now be added to a `MoleculeTable` with `addClusters` and retrieved with
-  `getClusters`, similar to scaffolds.
+- added a tutorial on model and data serialization
 
 ## Removed Features
 
-- removed support for PyBoost since the project was abandoned by the original developers and is [no longer maintained](https://github.com/sb-ai-lab/Py-Boost/graphs/contributors)
+
diff --git a/tutorials/README.md b/tutorials/README.md
index 67f3fc63..644e5a93 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -44,9 +44,11 @@ the [documentation pages](https://cddleiden.github.io/QSPRpred/docs/).
         - [Logging](basics/modelling/logging.ipynb): How to set-up logging.
         - [Model Assessment](basics/modelling/model_assessment.ipynb): How to assess the
           performance of a model.
-    - Benchmarking
-        - [Benchmarking](basics/benchmarking/benchmarking.ipynb): How to benchmark
+    - Other
+        - [Benchmarking](basics/other/benchmarking.ipynb): How to benchmark
           QSPRpred.
+        - [Serialization](basics/other/serialization.ipynb): How to save and
+          load datasets and models.
 - **Advanced**
     - Data
         - [Parallelization](advanced/data/parallelization.ipynb): How to parallelize
diff --git a/tutorials/basics/benchmarking/benchmarking.ipynb b/tutorials/basics/other/benchmarking.ipynb
similarity index 100%
rename from tutorials/basics/benchmarking/benchmarking.ipynb
rename to tutorials/basics/other/benchmarking.ipynb
diff --git a/tutorials/basics/other/serialization.ipynb b/tutorials/basics/other/serialization.ipynb
new file mode 100644
index 00000000..a6eab81d
--- /dev/null
+++ b/tutorials/basics/other/serialization.ipynb
@@ -0,0 +1,1041 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Serialization of models and dataset\n",
+    "You may have already seen some examples of how to save and load QSPRpred models and datasets in other tutorials.\n",
+    "In this tutorial, we will go through the serialization of models and datasets in some more detail.\n",
+    "As well as saving and loading models and datasets, we will also cover what is contained in the saved files and how to use them.\n",
+    "\n",
+    "\n",
+    "## Dataset\n",
+    "First, we will create a simple `QSPRDataset` from our tutorial dataset tsv file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>pchembl_value_Mean</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th>pchembl_value_Mean_original</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0000</th>\n",
+       "      <td>Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>SerializationTutorialDataset_0000</td>\n",
+       "      <td>8.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0001</th>\n",
+       "      <td>Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>SerializationTutorialDataset_0001</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0002</th>\n",
+       "      <td>O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0002</td>\n",
+       "      <td>5.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0003</th>\n",
+       "      <td>CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0003</td>\n",
+       "      <td>5.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0004</th>\n",
+       "      <td>CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>SerializationTutorialDataset_0004</td>\n",
+       "      <td>5.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_4077</th>\n",
+       "      <td>CNc1ncc(C(=O)NCc2ccc(OC)cc2)c2nc(-c3ccco3)nn12</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>2018.0</td>\n",
+       "      <td>SerializationTutorialDataset_4077</td>\n",
+       "      <td>7.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_4078</th>\n",
+       "      <td>Nc1nc(-c2ccco2)c2ncn(C(=O)NCCc3ccccc3)c2n1</td>\n",
+       "      <td>8.22</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>SerializationTutorialDataset_4078</td>\n",
+       "      <td>8.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_4079</th>\n",
+       "      <td>Nc1nc(Nc2ccc(F)cc2)nc(CSc2nnc(N)s2)n1</td>\n",
+       "      <td>4.89</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>SerializationTutorialDataset_4079</td>\n",
+       "      <td>4.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_4080</th>\n",
+       "      <td>CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1</td>\n",
+       "      <td>6.51</td>\n",
+       "      <td>2013.0</td>\n",
+       "      <td>SerializationTutorialDataset_4080</td>\n",
+       "      <td>6.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_4081</th>\n",
+       "      <td>CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>SerializationTutorialDataset_4081</td>\n",
+       "      <td>7.35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4082 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                              SMILES  \\\n",
+       "QSPRID                                                                                 \n",
+       "SerializationTutorialDataset_0000  Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...   \n",
+       "SerializationTutorialDataset_0001  Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...   \n",
+       "SerializationTutorialDataset_0002   O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1   \n",
+       "SerializationTutorialDataset_0003  CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...   \n",
+       "SerializationTutorialDataset_0004  CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...   \n",
+       "...                                                                              ...   \n",
+       "SerializationTutorialDataset_4077     CNc1ncc(C(=O)NCc2ccc(OC)cc2)c2nc(-c3ccco3)nn12   \n",
+       "SerializationTutorialDataset_4078         Nc1nc(-c2ccco2)c2ncn(C(=O)NCCc3ccccc3)c2n1   \n",
+       "SerializationTutorialDataset_4079              Nc1nc(Nc2ccc(F)cc2)nc(CSc2nnc(N)s2)n1   \n",
+       "SerializationTutorialDataset_4080      CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1   \n",
+       "SerializationTutorialDataset_4081           CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12   \n",
+       "\n",
+       "                                   pchembl_value_Mean    Year  \\\n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                8.68  2008.0   \n",
+       "SerializationTutorialDataset_0001                4.82  2010.0   \n",
+       "SerializationTutorialDataset_0002                5.65  2009.0   \n",
+       "SerializationTutorialDataset_0003                5.45  2009.0   \n",
+       "SerializationTutorialDataset_0004                5.20  2019.0   \n",
+       "...                                               ...     ...   \n",
+       "SerializationTutorialDataset_4077                7.09  2018.0   \n",
+       "SerializationTutorialDataset_4078                8.22  2008.0   \n",
+       "SerializationTutorialDataset_4079                4.89  2010.0   \n",
+       "SerializationTutorialDataset_4080                6.51  2013.0   \n",
+       "SerializationTutorialDataset_4081                7.35  2014.0   \n",
+       "\n",
+       "                                                              QSPRID  \\\n",
+       "QSPRID                                                                 \n",
+       "SerializationTutorialDataset_0000  SerializationTutorialDataset_0000   \n",
+       "SerializationTutorialDataset_0001  SerializationTutorialDataset_0001   \n",
+       "SerializationTutorialDataset_0002  SerializationTutorialDataset_0002   \n",
+       "SerializationTutorialDataset_0003  SerializationTutorialDataset_0003   \n",
+       "SerializationTutorialDataset_0004  SerializationTutorialDataset_0004   \n",
+       "...                                                              ...   \n",
+       "SerializationTutorialDataset_4077  SerializationTutorialDataset_4077   \n",
+       "SerializationTutorialDataset_4078  SerializationTutorialDataset_4078   \n",
+       "SerializationTutorialDataset_4079  SerializationTutorialDataset_4079   \n",
+       "SerializationTutorialDataset_4080  SerializationTutorialDataset_4080   \n",
+       "SerializationTutorialDataset_4081  SerializationTutorialDataset_4081   \n",
+       "\n",
+       "                                   pchembl_value_Mean_original  \n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                         8.68  \n",
+       "SerializationTutorialDataset_0001                         4.82  \n",
+       "SerializationTutorialDataset_0002                         5.65  \n",
+       "SerializationTutorialDataset_0003                         5.45  \n",
+       "SerializationTutorialDataset_0004                         5.20  \n",
+       "...                                                        ...  \n",
+       "SerializationTutorialDataset_4077                         7.09  \n",
+       "SerializationTutorialDataset_4078                         8.22  \n",
+       "SerializationTutorialDataset_4079                         4.89  \n",
+       "SerializationTutorialDataset_4080                         6.51  \n",
+       "SerializationTutorialDataset_4081                         7.35  \n",
+       "\n",
+       "[4082 rows x 5 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load a dataset from a table file\n",
+    "import os\n",
+    "\n",
+    "from IPython.display import display\n",
+    "\n",
+    "from qsprpred.data import QSPRDataset\n",
+    "\n",
+    "os.makedirs(\"../../tutorial_output/data\", exist_ok=True)\n",
+    "\n",
+    "dataset = QSPRDataset.fromTableFile(\n",
+    "    filename=\"../../tutorial_data/A2A_LIGANDS.tsv\",\n",
+    "    store_dir=\"../../tutorial_output/data\",\n",
+    "    name=\"SerializationTutorialDataset\",\n",
+    "    target_props=[{\"name\": \"pchembl_value_Mean\", \"task\": \"regression\"}],\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "display(dataset.getDF())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even though we specified the folder and dataset name, the dataset is not saved to disk until we call the `save` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.save()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you will notice that a new folder has been created in the specified directory called `SerializationTutorialDataset`.\n",
+    "This folder contains the dataset file `SerializationTutorialDataset_df.pkl` and the metadata file `SerializationTutorialDataset_meta.json`.\n",
+    "The dataset file is a pickle file that contains the dataset as a pandas DataFrame, which we showed above with `getDF()`.\n",
+    "The metadata file is a json file that contains the metadata of the dataset, such as the dataset name, index columns and target properties.\n",
+    "\n",
+    "Next, we will add some descriptors to the dataset and save it to disk again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qsprpred.data.descriptors.fingerprints import MorganFP\n",
+    "\n",
+    "dataset.addDescriptors([MorganFP(radius=3, nBits=2048)])\n",
+    "\n",
+    "dataset.save()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can see that the dataset folder contains an additional folder called `Descriptors_SerializationTutorialDataset_MorganFP` which contains the descriptor files.\n",
+    "The descriptors are again saved as a pickle file and metadata file:\n",
+    "- `Descriptors_SerializationTutorialDataset_MorganFP_df.pkl` contains the descriptors as a pandas DataFrame.\n",
+    "- `Descriptors_SerializationTutorialDataset_MorganFP_meta.json` contains the metadata of the descriptors, such as the descriptor name and type.\n",
+    "\n",
+    "If we were to add more descriptors to the dataset, they would be saved in the same way in a new folder with the descriptor name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is two ways to reload the dataset from disk:\n",
+    "\n",
+    "The first one is call the fromFile method on the metadata file of the QSPRDataset class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>pchembl_value_Mean</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th>pchembl_value_Mean_original</th>\n",
+       "      <th>Split_IsTrain</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0000</th>\n",
+       "      <td>Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>SerializationTutorialDataset_0000</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0001</th>\n",
+       "      <td>Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>SerializationTutorialDataset_0001</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0002</th>\n",
+       "      <td>O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0002</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0003</th>\n",
+       "      <td>CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0003</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0004</th>\n",
+       "      <td>CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>SerializationTutorialDataset_0004</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                              SMILES  \\\n",
+       "QSPRID                                                                                 \n",
+       "SerializationTutorialDataset_0000  Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...   \n",
+       "SerializationTutorialDataset_0001  Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...   \n",
+       "SerializationTutorialDataset_0002   O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1   \n",
+       "SerializationTutorialDataset_0003  CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...   \n",
+       "SerializationTutorialDataset_0004  CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...   \n",
+       "\n",
+       "                                   pchembl_value_Mean    Year  \\\n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                8.68  2008.0   \n",
+       "SerializationTutorialDataset_0001                4.82  2010.0   \n",
+       "SerializationTutorialDataset_0002                5.65  2009.0   \n",
+       "SerializationTutorialDataset_0003                5.45  2009.0   \n",
+       "SerializationTutorialDataset_0004                5.20  2019.0   \n",
+       "\n",
+       "                                                              QSPRID  \\\n",
+       "QSPRID                                                                 \n",
+       "SerializationTutorialDataset_0000  SerializationTutorialDataset_0000   \n",
+       "SerializationTutorialDataset_0001  SerializationTutorialDataset_0001   \n",
+       "SerializationTutorialDataset_0002  SerializationTutorialDataset_0002   \n",
+       "SerializationTutorialDataset_0003  SerializationTutorialDataset_0003   \n",
+       "SerializationTutorialDataset_0004  SerializationTutorialDataset_0004   \n",
+       "\n",
+       "                                   pchembl_value_Mean_original  Split_IsTrain  \n",
+       "QSPRID                                                                         \n",
+       "SerializationTutorialDataset_0000                         8.68           True  \n",
+       "SerializationTutorialDataset_0001                         4.82           True  \n",
+       "SerializationTutorialDataset_0002                         5.65           True  \n",
+       "SerializationTutorialDataset_0003                         5.45           True  \n",
+       "SerializationTutorialDataset_0004                         5.20           True  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = QSPRDataset.fromFile(\"../../tutorial_output/data/SerializationTutorialDataset/SerializationTutorialDataset_meta.json\")\n",
+    "\n",
+    "dataset.getDF().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second one is to create a new `QSPRDataset` object with the same name and folder as the dataset that we want to load."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>pchembl_value_Mean</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th>pchembl_value_Mean_original</th>\n",
+       "      <th>Split_IsTrain</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0000</th>\n",
+       "      <td>Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>SerializationTutorialDataset_0000</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0001</th>\n",
+       "      <td>Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>SerializationTutorialDataset_0001</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0002</th>\n",
+       "      <td>O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0002</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0003</th>\n",
+       "      <td>CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0003</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0004</th>\n",
+       "      <td>CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>SerializationTutorialDataset_0004</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                              SMILES  \\\n",
+       "QSPRID                                                                                 \n",
+       "SerializationTutorialDataset_0000  Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...   \n",
+       "SerializationTutorialDataset_0001  Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...   \n",
+       "SerializationTutorialDataset_0002   O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1   \n",
+       "SerializationTutorialDataset_0003  CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...   \n",
+       "SerializationTutorialDataset_0004  CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...   \n",
+       "\n",
+       "                                   pchembl_value_Mean    Year  \\\n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                8.68  2008.0   \n",
+       "SerializationTutorialDataset_0001                4.82  2010.0   \n",
+       "SerializationTutorialDataset_0002                5.65  2009.0   \n",
+       "SerializationTutorialDataset_0003                5.45  2009.0   \n",
+       "SerializationTutorialDataset_0004                5.20  2019.0   \n",
+       "\n",
+       "                                                              QSPRID  \\\n",
+       "QSPRID                                                                 \n",
+       "SerializationTutorialDataset_0000  SerializationTutorialDataset_0000   \n",
+       "SerializationTutorialDataset_0001  SerializationTutorialDataset_0001   \n",
+       "SerializationTutorialDataset_0002  SerializationTutorialDataset_0002   \n",
+       "SerializationTutorialDataset_0003  SerializationTutorialDataset_0003   \n",
+       "SerializationTutorialDataset_0004  SerializationTutorialDataset_0004   \n",
+       "\n",
+       "                                   pchembl_value_Mean_original  Split_IsTrain  \n",
+       "QSPRID                                                                         \n",
+       "SerializationTutorialDataset_0000                         8.68           True  \n",
+       "SerializationTutorialDataset_0001                         4.82           True  \n",
+       "SerializationTutorialDataset_0002                         5.65           True  \n",
+       "SerializationTutorialDataset_0003                         5.45           True  \n",
+       "SerializationTutorialDataset_0004                         5.20           True  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = QSPRDataset(\n",
+    "    name=\"SerializationTutorialDataset\",\n",
+    "    store_dir=\"../../tutorial_output/data/\",\n",
+    ")\n",
+    "\n",
+    "dataset.getDF().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to save the dataset as a tsv file, by specifing the `store_format` parameter as 'csv'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SMILES</th>\n",
+       "      <th>pchembl_value_Mean</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th>pchembl_value_Mean_original</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0000</th>\n",
+       "      <td>Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>SerializationTutorialDataset_0000</td>\n",
+       "      <td>8.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0001</th>\n",
+       "      <td>Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...</td>\n",
+       "      <td>4.82</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>SerializationTutorialDataset_0001</td>\n",
+       "      <td>4.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0002</th>\n",
+       "      <td>O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1</td>\n",
+       "      <td>5.65</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0002</td>\n",
+       "      <td>5.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0003</th>\n",
+       "      <td>CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...</td>\n",
+       "      <td>5.45</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>SerializationTutorialDataset_0003</td>\n",
+       "      <td>5.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SerializationTutorialDataset_0004</th>\n",
+       "      <td>CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...</td>\n",
+       "      <td>5.20</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>SerializationTutorialDataset_0004</td>\n",
+       "      <td>5.20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                              SMILES  \\\n",
+       "QSPRID                                                                                 \n",
+       "SerializationTutorialDataset_0000  Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...   \n",
+       "SerializationTutorialDataset_0001  Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...   \n",
+       "SerializationTutorialDataset_0002   O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1   \n",
+       "SerializationTutorialDataset_0003  CNC(=O)C12CC1C(n1cnc3c1nc(C#CCCCCC(=O)OC)nc3NC...   \n",
+       "SerializationTutorialDataset_0004  CCCn1c(=O)c2c(nc3cc(OC)ccn32)n(CCCNC(=O)c2ccc(...   \n",
+       "\n",
+       "                                   pchembl_value_Mean    Year  \\\n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                8.68  2008.0   \n",
+       "SerializationTutorialDataset_0001                4.82  2010.0   \n",
+       "SerializationTutorialDataset_0002                5.65  2009.0   \n",
+       "SerializationTutorialDataset_0003                5.45  2009.0   \n",
+       "SerializationTutorialDataset_0004                5.20  2019.0   \n",
+       "\n",
+       "                                                              QSPRID  \\\n",
+       "QSPRID                                                                 \n",
+       "SerializationTutorialDataset_0000  SerializationTutorialDataset_0000   \n",
+       "SerializationTutorialDataset_0001  SerializationTutorialDataset_0001   \n",
+       "SerializationTutorialDataset_0002  SerializationTutorialDataset_0002   \n",
+       "SerializationTutorialDataset_0003  SerializationTutorialDataset_0003   \n",
+       "SerializationTutorialDataset_0004  SerializationTutorialDataset_0004   \n",
+       "\n",
+       "                                   pchembl_value_Mean_original  \n",
+       "QSPRID                                                          \n",
+       "SerializationTutorialDataset_0000                         8.68  \n",
+       "SerializationTutorialDataset_0001                         4.82  \n",
+       "SerializationTutorialDataset_0002                         5.65  \n",
+       "SerializationTutorialDataset_0003                         5.45  \n",
+       "SerializationTutorialDataset_0004                         5.20  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load a dataset from a table file\n",
+    "import os\n",
+    "\n",
+    "from IPython.display import display\n",
+    "\n",
+    "from qsprpred.data import QSPRDataset\n",
+    "\n",
+    "os.makedirs(\"../../tutorial_output/data\", exist_ok=True)\n",
+    "\n",
+    "dataset = QSPRDataset.fromTableFile(\n",
+    "    filename=\"../../tutorial_data/A2A_LIGANDS.tsv\",\n",
+    "    store_dir=\"../../tutorial_output/data\",\n",
+    "    name=\"SerializationTutorialDataset\",\n",
+    "    target_props=[{\"name\": \"pchembl_value_Mean\", \"task\": \"REGRESSION\"}],\n",
+    "    random_state=42,\n",
+    "    store_format=\"csv\",\n",
+    "    overwrite=True\n",
+    ")\n",
+    "\n",
+    "display(dataset.getDF().head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we also specified the `overwrite = True` parameter, which will overwrite the existing dataset file if it already exists.\n",
+    "Notice. that the dataset folder has now been deleted, but no new folder has been created yet.\n",
+    "This is because the dataset is not saved to disk until we call the `save` method.\n",
+    "\n",
+    "After we have added a descriptor set and save again, you can see that the dataset file has now been saved as a csv file.\n",
+    "Moreover, the descriptor folder has been created and contains the descriptor file now also saved as a csv file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.addDescriptors([MorganFP(radius=3, nBits=2048)])\n",
+    "\n",
+    "dataset.save()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Models\n",
+    "\n",
+    "Now we will create a simple `QSPRModel` from the tutorial dataset. This\n",
+    "time we notice the output folder has been created before we call the `save` method.\n",
+    "However, the folder is still empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qsprpred.models import SklearnModel\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "\n",
+    "os.makedirs(\"../../tutorial_output/models\", exist_ok=True)\n",
+    "\n",
+    "model = SklearnModel(\n",
+    "    base_dir=\"../../tutorial_output/models\",\n",
+    "    alg=KNeighborsRegressor,\n",
+    "    name=\"SerializationTutorialModel\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we now call the `save` method, the model will be saved to disk.\n",
+    "The model folder contains the model file `SerializationTutorialModel.json` and the metadata file `SerializationTutorialModel_meta.json`.\n",
+    "The model file is a json file that contains the unfitted sci-kit learn model as a dictionary.\n",
+    "This json file is created with the [ml2json](https://github.com/OlivierBeq/ml2json).\n",
+    "Non scikit-learn type models can be saved as different file types, such as models described in the [deep learning models tutorial](../../advanced/modelling/deep_learning_models.ipynb) or the [chemprop models tutorial](../../advanced/modelling/chemprop_models.ipynb) which will be saved as a pickle file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/zfsdata/data/helle/01_MainProjects/03_QSPRpred/Scripts/QSPRpred/tutorials/tutorial_output/models/SerializationTutorialModel/SerializationTutorialModel_meta.json'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.save()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we then run cross-validation and train on the whole dataset making predictions for the independent test set, this will add\n",
+    "two tsv files to the model folder:\n",
+    "- `SerializationTutorialModel.cv.tsv` contains the cross-validation predictions.\n",
+    "- `SerializationTutorialModel.ind.tsv` contains the independent test set predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.6306765])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qsprpred.data import RandomSplit\n",
+    "from qsprpred.models import CrossValAssessor, TestSetAssessor\n",
+    "# Split the dataset into training and test sets\n",
+    "dataset.split(RandomSplit(test_fraction=0.2, seed=42))\n",
+    "\n",
+    "# We can now assess the model performance on the training set using cross validation\n",
+    "CrossValAssessor(\"r2\")(model, dataset)\n",
+    "\n",
+    "# and on the test set\n",
+    "TestSetAssessor(\"r2\")(model, dataset)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These files are used by the plotting methods of the `QSPRModel` class to plot the predictions.\n",
+    "However, you can also use these files to plot the predictions yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>QSPRID</th>\n",
+       "      <th>pchembl_value_Mean_Label</th>\n",
+       "      <th>pchembl_value_Mean_Prediction</th>\n",
+       "      <th>Fold</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SerializationTutorialDataset_0221</td>\n",
+       "      <td>7.20</td>\n",
+       "      <td>7.610</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SerializationTutorialDataset_3260</td>\n",
+       "      <td>6.02</td>\n",
+       "      <td>6.078</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SerializationTutorialDataset_3675</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>8.116</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SerializationTutorialDataset_3330</td>\n",
+       "      <td>8.02</td>\n",
+       "      <td>7.055</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SerializationTutorialDataset_3484</td>\n",
+       "      <td>6.77</td>\n",
+       "      <td>7.636</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              QSPRID  pchembl_value_Mean_Label  \\\n",
+       "0  SerializationTutorialDataset_0221                      7.20   \n",
+       "1  SerializationTutorialDataset_3260                      6.02   \n",
+       "2  SerializationTutorialDataset_3675                      8.32   \n",
+       "3  SerializationTutorialDataset_3330                      8.02   \n",
+       "4  SerializationTutorialDataset_3484                      6.77   \n",
+       "\n",
+       "   pchembl_value_Mean_Prediction  Fold  \n",
+       "0                          7.610   0.0  \n",
+       "1                          6.078   0.0  \n",
+       "2                          8.116   0.0  \n",
+       "3                          7.055   0.0  \n",
+       "4                          7.636   0.0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='pchembl_value_Mean_Label', ylabel='pchembl_value_Mean_Prediction'>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df_cv = pd.read_csv(\"../../tutorial_output/models/SerializationTutorialModel/SerializationTutorialModel.cv.tsv\", sep=\"\\t\")\n",
+    "\n",
+    "display(df_cv.head())\n",
+    "\n",
+    "import seaborn as sns\n",
+    "sns.scatterplot(data=df_cv, x=\"pchembl_value_Mean_Label\", y=\"pchembl_value_Mean_Prediction\", hue=df_cv.Fold.astype(str))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One thing to note is that if we now have a look at the model file (`SerializationTutorialModel.json`), we can see that the model is not yet fitted.\n",
+    "By default the `estimator`, which is the underlying scikit-learn model, is not saved to the model file until you call the `fitDataset` method, which fits the whole (including the independent test set) dataset to the model. If you do want to have access to the estimators fitted during cross-validation and test set analysis, you can use monitors as described in the [monitoring tutorial](../../advanced/modelling/monitoring.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/zfsdata/data/helle/01_MainProjects/03_QSPRpred/Scripts/QSPRpred/tutorials/tutorial_output/models/SerializationTutorialModel/SerializationTutorialModel_meta.json'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fitDataset(dataset)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "qsprpred",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}