Fix/tutorials execution

tutorials/tutorial_clustering_tree_explainer.ipynb

@@ -9,7 +9,20 @@
     "# Clustering Tree Explainer\n",
     "\n",
     "\n",
-    "When executing a clustering algorithm like K-Means usually the samples in our dataset are partitioned to K different clusters/groups. However, sometimes is difficult to understand why a sample is assigned to a particular cluster or what are the features that characterize a cluster. To improve interpretability, a small decision tree can be used to partition the data in the assigned clusters of a previously run clustering algorithm. In this tutorial, we show how we can build this decision tree using the `ClusteringTreeExplainer` class. This class builds a decision tree based on the [Iterative Mistake Minimization (IMM)](https://arxiv.org/pdf/2002.12538.pdf) method and [ExKMC: Expanding Explainable k-Means Clustering] (https://arxiv.org/pdf/2006.02399.pdf)\n"
+    "When executing a clustering algorithm like K-Means usually the samples in our dataset are partitioned to K different clusters/groups. However, sometimes is difficult to understand why a sample is assigned to a particular cluster or what are the features that characterize a cluster. To improve interpretability, a small decision tree can be used to partition the data in the assigned clusters of a previously run clustering algorithm. In this tutorial, we show how we can build this decision tree using the `ClusteringTreeExplainer` class. This class builds a decision tree based on the [Iterative Mistake Minimization (IMM)](https://arxiv.org/pdf/2002.12538.pdf) method and [ExKMC: Expanding Explainable k-Means Clustering](https://arxiv.org/pdf/2006.02399.pdf)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "To execute this tutorial, you need to install mercury-explainability and pyspark (if they are not already installed). You can install them by executing the next command in a cell:\n",
+    "\n",
+    "```\n",
+    "!pip install mercury-explainability pyspark\n",
+    "```"
    ]
   },
   {
@@ -1295,5 +1308,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }