diff --git a/Pipfile b/Pipfile
index 25da2c20..1e267716 100644
--- a/Pipfile
+++ b/Pipfile
@@ -7,6 +7,8 @@ name = "pypi"
 networkx = "*"
 numpy = "*"
 scikit-learn = "*"
+shap = "*"
+xarray = "*"
 
 [dev-packages]
 pytest = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index 1151d408..0addcd89 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "4f36f76cfc52dc83b74cc00b92af24b8ff556ca22dc2f21401224c39659346eb"
+            "sha256": "eccfc9563430b35e4b8e92aea9e1b387fea449e1538b8162224087bad096ab71"
         },
         "pipfile-spec": 6,
         "requires": {},
@@ -14,6 +14,14 @@
         ]
     },
     "default": {
+        "cloudpickle": {
+            "hashes": [
+                "sha256:246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7",
+                "sha256:996d9a482c6fb4f33c1a35335cf8afd065d2a56e973270364840712d9131a882"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==3.0.0"
+        },
         "joblib": {
             "hashes": [
                 "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1",
@@ -22,105 +30,298 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.3.2"
         },
+        "llvmlite": {
+            "hashes": [
+                "sha256:04725975e5b2af416d685ea0769f4ecc33f97be541e301054c9f741003085802",
+                "sha256:0dd0338da625346538f1173a17cabf21d1e315cf387ca21b294ff209d176e244",
+                "sha256:150d0bc275a8ac664a705135e639178883293cf08c1a38de3bbaa2f693a0a867",
+                "sha256:1eee5cf17ec2b4198b509272cf300ee6577229d237c98cc6e63861b08463ddc6",
+                "sha256:210e458723436b2469d61b54b453474e09e12a94453c97ea3fbb0742ba5a83d8",
+                "sha256:2181bb63ef3c607e6403813421b46982c3ac6bfc1f11fa16a13eaafb46f578e6",
+                "sha256:24091a6b31242bcdd56ae2dbea40007f462260bc9bdf947953acc39dffd54f8f",
+                "sha256:2b76acee82ea0e9304be6be9d4b3840208d050ea0dcad75b1635fa06e949a0ae",
+                "sha256:2d92c51e6e9394d503033ffe3292f5bef1566ab73029ec853861f60ad5c925d0",
+                "sha256:5940bc901fb0325970415dbede82c0b7f3e35c2d5fd1d5e0047134c2c46b3281",
+                "sha256:8454c1133ef701e8c050a59edd85d238ee18bb9a0eb95faf2fca8b909ee3c89a",
+                "sha256:855f280e781d49e0640aef4c4af586831ade8f1a6c4df483fb901cbe1a48d127",
+                "sha256:880cb57ca49e862e1cd077104375b9d1dfdc0622596dfa22105f470d7bacb309",
+                "sha256:8b0a9a47c28f67a269bb62f6256e63cef28d3c5f13cbae4fab587c3ad506778b",
+                "sha256:92c32356f669e036eb01016e883b22add883c60739bc1ebee3a1cc0249a50828",
+                "sha256:92f093986ab92e71c9ffe334c002f96defc7986efda18397d0f08534f3ebdc4d",
+                "sha256:9564c19b31a0434f01d2025b06b44c7ed422f51e719ab5d24ff03b7560066c9a",
+                "sha256:b67340c62c93a11fae482910dc29163a50dff3dfa88bc874872d28ee604a83be",
+                "sha256:bf14aa0eb22b58c231243dccf7e7f42f7beec48970f2549b3a6acc737d1a4ba4",
+                "sha256:c1e1029d47ee66d3a0c4d6088641882f75b93db82bd0e6178f7bd744ebce42b9",
+                "sha256:df75594e5a4702b032684d5481db3af990b69c249ccb1d32687b8501f0689432",
+                "sha256:f19f767a018e6ec89608e1f6b13348fa2fcde657151137cb64e56d48598a92db",
+                "sha256:f8afdfa6da33f0b4226af8e64cfc2b28986e005528fbf944d0a24a72acfc9432",
+                "sha256:fa1469901a2e100c17eb8fe2678e34bd4255a3576d1a543421356e9c14d6e2ae"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==0.41.1"
+        },
         "networkx": {
             "hashes": [
-                "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36",
-                "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"
+                "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6",
+                "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"
             ],
             "index": "pypi",
+            "markers": "python_version >= '3.9'",
+            "version": "==3.2.1"
+        },
+        "numba": {
+            "hashes": [
+                "sha256:07f2fa7e7144aa6f275f27260e73ce0d808d3c62b30cff8906ad1dec12d87bbe",
+                "sha256:240e7a1ae80eb6b14061dc91263b99dc8d6af9ea45d310751b780888097c1aaa",
+                "sha256:45698b995914003f890ad839cfc909eeb9c74921849c712a05405d1a79c50f68",
+                "sha256:487ded0633efccd9ca3a46364b40006dbdaca0f95e99b8b83e778d1195ebcbaa",
+                "sha256:4e79b6cc0d2bf064a955934a2e02bf676bc7995ab2db929dbbc62e4c16551be6",
+                "sha256:55a01e1881120e86d54efdff1be08381886fe9f04fc3006af309c602a72bc44d",
+                "sha256:5c765aef472a9406a97ea9782116335ad4f9ef5c9f93fc05fd44aab0db486954",
+                "sha256:6fe7a9d8e3bd996fbe5eac0683227ccef26cba98dae6e5cee2c1894d4b9f16c1",
+                "sha256:7bf1ddd4f7b9c2306de0384bf3854cac3edd7b4d8dffae2ec1b925e4c436233f",
+                "sha256:811305d5dc40ae43c3ace5b192c670c358a89a4d2ae4f86d1665003798ea7a1a",
+                "sha256:81fe5b51532478149b5081311b0fd4206959174e660c372b94ed5364cfb37c82",
+                "sha256:898af055b03f09d33a587e9425500e5be84fc90cd2f80b3fb71c6a4a17a7e354",
+                "sha256:9e9356e943617f5e35a74bf56ff6e7cc83e6b1865d5e13cee535d79bf2cae954",
+                "sha256:a1eaa744f518bbd60e1f7ccddfb8002b3d06bd865b94a5d7eac25028efe0e0ff",
+                "sha256:bc2d904d0319d7a5857bd65062340bed627f5bfe9ae4a495aef342f072880d50",
+                "sha256:bcecd3fb9df36554b342140a4d77d938a549be635d64caf8bd9ef6c47a47f8aa",
+                "sha256:bd3dda77955be03ff366eebbfdb39919ce7c2620d86c906203bed92124989032",
+                "sha256:bf68df9c307fb0aa81cacd33faccd6e419496fdc621e83f1efce35cdc5e79cac",
+                "sha256:d3e2fe81fe9a59fcd99cc572002101119059d64d31eb6324995ee8b0f144a306",
+                "sha256:e63d6aacaae1ba4ef3695f1c2122b30fa3d8ba039c8f517784668075856d79e2",
+                "sha256:ea5bfcf7d641d351c6a80e8e1826eb4a145d619870016eeaf20bbd71ef5caa22"
+            ],
             "markers": "python_version >= '3.8'",
-            "version": "==3.1"
+            "version": "==0.58.1"
         },
         "numpy": {
             "hashes": [
-                "sha256:0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2",
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             ],
             "index": "pypi",
             "markers": "python_version >= '3.9'",
-            "version": "==1.25.2"
+            "version": "==1.26.3"
+        },
+        "packaging": {
+            "hashes": [
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+            ],
+            "markers": "python_version >= '3.9'",
+            "version": "==2.2.0"
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+        "python-dateutil": {
+            "hashes": [
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+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==2.8.2"
+        },
+        "pytz": {
+            "hashes": [
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+                "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"
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         "scikit-learn": {
             "hashes": [
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@@ -129,182 +330,310 @@
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+            "markers": "python_version >= '3.7'",
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         "docutils": {
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@@ -316,19 +645,19 @@
         },
         "flake8": {
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         "imagesize": {
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@@ -340,11 +669,11 @@
         },
         "importlib-metadata": {
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@@ -362,21 +691,29 @@
             "markers": "python_version >= '3.8'",
             "version": "==3.3.0"
         },
+        "jeepney": {
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@@ -388,59 +725,69 @@
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@@ -460,19 +807,40 @@
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@@ -484,19 +852,26 @@
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@@ -508,28 +883,28 @@
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@@ -558,11 +933,11 @@
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@@ -590,27 +965,27 @@
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@@ -638,27 +1013,27 @@
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@@ -670,19 +1045,19 @@
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@@ -695,26 +1070,19 @@
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@@ -728,100 +1096,115 @@
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             ],
             "markers": "python_version >= '3.8'",
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         },
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+                "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.0.4"
+            "markers": "python_version >= '3.8'",
+            "version": "==2.1.0"
         }
     }
 }
diff --git a/hiclass/Explainer.py b/hiclass/Explainer.py
new file mode 100644
index 00000000..379bf09a
--- /dev/null
+++ b/hiclass/Explainer.py
@@ -0,0 +1,304 @@
+"""Explainer API for explaining predictions using shapley values."""
+
+from copy import deepcopy
+
+import numpy as np
+
+from hiclass import (
+    LocalClassifierPerParentNode,
+    LocalClassifierPerNode,
+    LocalClassifierPerLevel,
+    ConstantClassifier,
+)
+
+try:
+    import xarray as xr
+except ImportError:
+    xarray_installed = False
+else:
+    xarray_installed = True
+
+try:
+    import shap
+except ImportError:
+    shap_installed = False
+else:
+    shap_installed = True
+
+
+def _check_imports():
+    if not shap_installed:
+        raise ImportError(
+            "Shap is not installed. Please install it using `pip install shap` first."
+        )
+    elif not xarray_installed:
+        raise ImportError(
+            "xarray is not installed. Please install it using `pip install xarray` first."
+        )
+
+
+class Explainer:
+    """Explainer class for returning shap values for each of the three hierarchical classifiers."""
+
+    def __init__(self, hierarchical_model, data=None, algorithm="auto", mode=""):
+        """
+        Initialize the SHAP explainer for a hierarchical model.
+
+        Parameters
+        ----------
+        hierarchical_model : HierarchicalClassifier
+            The hierarchical classification model to explain.
+        data : array-like or None, default=None
+            The dataset used for creating the SHAP explainer.
+        algorithm : str, default="auto"
+            The algorithm to use for SHAP explainer.
+        mode : str, default=""
+            The mode of the SHAP explainer. Can be 'tree', 'gradient', 'deep', 'linear', or '' for default SHAP explainer.
+        """
+        self.hierarchical_model = hierarchical_model
+        self.algorithm = algorithm
+        self.mode = mode
+        self.data = data
+
+        _check_imports()
+
+        if mode == "tree":
+            self.explainer = shap.TreeExplainer
+        elif mode == "gradient":
+            self.explainer = shap.GradientExplainer
+        elif mode == "deep":
+            self.explainer = shap.DeepExplainer
+        elif mode == "linear":
+            self.explainer = shap.LinearExplainer
+        else:
+            self.explainer = shap.Explainer
+
+    def explain(self, X):
+        """
+        Generate SHAP values for each node in the hierarchy for the given data.
+
+        Parameters
+        ----------
+        X : array-like
+            Training data to fit the SHAP explainer.
+
+        Returns
+        -------
+        shap_values_dict : dict
+            A dictionary of SHAP values for each node.
+        """
+        _check_imports()
+        if isinstance(self.hierarchical_model, LocalClassifierPerParentNode):
+            return self._explain_with_xr(X)
+        elif isinstance(self.hierarchical_model, LocalClassifierPerLevel):
+            return self._explain_lcpl(X)
+        elif isinstance(self.hierarchical_model, LocalClassifierPerNode):
+            return self._explain_lcpn(X)
+        else:
+            raise ValueError(f"Invalid model: {self.hierarchical_model}.")
+
+    def _explain_with_dict(self, X):
+        """
+        Generate SHAP values for each node using Local Classifier Per Parent Node (LCPPN) strategy.
+
+        Parameters
+        ----------
+        X : array-like
+            Sample data for which to generate SHAP values.
+
+        traverse_prediction : True or False
+            If True, restricts calculation of shap values to only traversed hierarchy as predicted by hiclass model.
+
+        Returns
+        -------
+        shap_values_dict : dict
+            A dictionary of SHAP values for each node.
+        """
+        shap_values_dict = {}
+        traversed_nodes = self._get_traversed_nodes(X)
+        for node in traversed_nodes:
+            local_classifier = self.hierarchical_model.hierarchy_.nodes[node][
+                "classifier"
+            ]
+
+            # Create explainer with train data
+            local_explainer = deepcopy(self.explainer)(local_classifier, self.data)
+            shap_values = np.array(local_explainer.shap_values(X))
+
+            if len(shap_values.shape) < 3:
+                shap_values = shap_values.reshape(
+                    1, shap_values.shape[0], shap_values.shape[1]
+                )
+
+            shap_values_dict[node] = shap_values
+
+        for node in self.hierarchical_model.hierarchy_.nodes:
+            if node not in traversed_nodes:
+                local_classifier = self.hierarchical_model.hierarchy_.nodes[node]
+                if len(local_classifier) != 0:
+                    shap_val = np.full(
+                        (
+                            len(local_classifier["classifier"].classes_),
+                            X.shape[0],
+                            X.shape[1],
+                        ),
+                        np.nan,
+                    )
+                    shap_values_dict[node] = shap_val
+        return shap_values_dict
+
+    def _explain_with_xr(self, X):
+        """
+        Generate SHAP values for each node using Local Classifier Per Parent Node (LCPPN) strategy.
+
+        Parameters
+        ----------
+        X : array-like
+            Sample data for which to generate SHAP values.
+
+        Returns
+        -------
+        shap_values_dict : dict
+            A dictionary of SHAP values for each node.
+        """
+        explanations = []
+        for sample in X:
+            explanation = self._calculate_shap_values(sample.reshape(1, -1))
+            explanations.append(explanation)
+        return explanations
+
+    def _explain_lcpn(self, X):
+        shap_values_dict = {}
+        for node in self.hierarchical_model.hierarchy_.nodes:
+            if node == self.hierarchical_model.root_:
+                continue
+
+            if isinstance(
+                self.hierarchical_model.hierarchy_.nodes[node]["classifier"],
+                ConstantClassifier.ConstantClassifier,
+            ):
+                continue
+
+            local_classifier = self.hierarchical_model.hierarchy_.nodes[node][
+                "classifier"
+            ]
+
+            local_explainer = deepcopy(self.explainer)(local_classifier, self.data)
+
+            # Calculate SHAP values for the given sample X
+            shap_values = np.array(local_explainer.shap_values(X))
+            shap_values_dict[node] = shap_values
+
+        return shap_values_dict
+
+    def _explain_lcpl(self, X):
+        """
+        Generate SHAP values for each node using Local Classifier Per Level (LCPL) strategy.
+
+        Parameters
+        ----------
+        X : array-like
+            Sample data for which to generate SHAP values.
+
+        Returns
+        -------
+        shap_values_dict : dict
+            A dictionary of SHAP values for each node.
+        """
+        shap_values_dict = {}
+        start_level = 1
+        if len(self.hierarchical_model.local_classifiers_[start_level]) == 1:
+            start_level = 2
+
+        for level in range(
+            start_level, len(self.hierarchical_model.local_classifiers_)
+        ):
+            local_classifier = self.hierarchical_model.local_classifiers_[level]
+            local_explainer = deepcopy(self.explainer)(local_classifier, self.data)
+
+            # Calculate SHAP values for the given sample X
+            shap_values = np.array(local_explainer.shap_values(X))
+            shap_values_dict[level] = shap_values
+
+        return shap_values_dict
+
+    def _get_traversed_nodes(self, samples):
+        # Helper function to return traversed nodes
+        if isinstance(self.hierarchical_model, LocalClassifierPerParentNode):
+            traversals = []
+            start_node = self.hierarchical_model.root_
+            for x in samples:
+                current = start_node
+                traversal_order = []
+                while self.hierarchical_model.hierarchy_.neighbors(current):
+                    if (
+                        "classifier"
+                        not in self.hierarchical_model.hierarchy_.nodes[current]
+                    ):
+                        break  # Break if reached leaf node
+                    traversal_order.append(current)
+                    successor = self.hierarchical_model.hierarchy_.nodes[current][
+                        "classifier"
+                    ].predict(x.reshape(1, -1))[0]
+                    current = successor
+                traversals.append(traversal_order)
+            return traversals
+        elif isinstance(self.hierarchical_model, LocalClassifierPerNode):
+            pass
+        elif isinstance(self.hierarchical_model, LocalClassifierPerLevel):
+            pass
+
+    def _calculate_shap_values(self, X):
+        if not xarray_installed:
+            raise ImportError(
+                "xarray is not installed. Please install it using `pip install xarray` before using "
+                "this method."
+            )
+        traversed_nodes = self._get_traversed_nodes(X)[0]
+        datasets = []
+        for node in traversed_nodes:
+            local_classifier = self.hierarchical_model.hierarchy_.nodes[node][
+                "classifier"
+            ]
+
+            # Create a SHAP explainer for the local classifier
+            local_explainer = deepcopy(self.explainer)(local_classifier, self.data)
+
+            # Calculate SHAP values for the given sample X
+            shap_values = np.array(
+                local_explainer.shap_values(X, check_additivity=False)
+            )
+            if len(shap_values.shape) < 3:
+                shap_values = shap_values.reshape(
+                    1, shap_values.shape[0], shap_values.shape[1]
+                )
+
+            predict_proba = xr.DataArray(
+                local_classifier.predict_proba(X)[0],
+                dims=["class"],
+                coords={
+                    "class": local_classifier.classes_,
+                },
+            )
+            classes = xr.DataArray(
+                local_classifier.classes_,
+                dims=["class"],
+                coords={"class": local_classifier.classes_},
+            )
+
+            shap_val_local = xr.DataArray(
+                shap_values,
+                dims=["class", "sample", "feature"],
+            )
+
+            local_dataset = xr.Dataset(
+                {
+                    "node": node.split(self.hierarchical_model.separator_)[-1],
+                    "predicted_class": local_classifier.predict(X),
+                    "predict_proba": predict_proba,
+                    "classes": classes,
+                    "shap_values": shap_val_local,
+                }
+            )
+            datasets.append(local_dataset)
+        return datasets
diff --git a/hiclass/LocalClassifierPerParentNode.py b/hiclass/LocalClassifierPerParentNode.py
index 50a30ca0..e7755a23 100644
--- a/hiclass/LocalClassifierPerParentNode.py
+++ b/hiclass/LocalClassifierPerParentNode.py
@@ -224,3 +224,44 @@ def _fit_digraph(self, local_mode: bool = False, use_joblib: bool = False):
         self.logger_.info("Fitting local classifiers")
         nodes = self._get_parents()
         self._fit_node_classifier(nodes, local_mode, use_joblib)
+
+    def get_predict_proba(self, X):
+        """
+        Prediction probabilities for each class for the given data.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        Returns
+        -------
+        prediction_probabilities : dict or list of dicts if X contains multiple samples.
+            Each dict contains the node as its key and prediction probabilities for
+            the predicted classes as value.
+        """
+        prediction_probabilities = []
+        for sample in X:
+            predict_proba_dict = {}
+            y_pred = self.predict(sample.reshape(1, -1))
+            traversal_path = str(y_pred[0][0])
+            for pred in y_pred[0][1:]:
+                traversal_path = traversal_path + self.separator_ + pred
+
+            for i in range(self.max_levels_)[:-1]:
+                node = self.separator_.join(
+                    traversal_path.split(self.separator_)[: i + 1]
+                )
+
+                local_classifier = self.hierarchy_.nodes[node]["classifier"]
+                pred_probabilities = local_classifier.predict_proba(
+                    sample.reshape(1, -1)
+                )
+                predict_proba_dict[node] = pred_probabilities
+            prediction_probabilities.append(predict_proba_dict)
+
+        if len(prediction_probabilities) == 1:
+            return prediction_probabilities[0]
+
+        return prediction_probabilities
diff --git a/hiclass/__init__.py b/hiclass/__init__.py
index 09370436..ec3db00e 100644
--- a/hiclass/__init__.py
+++ b/hiclass/__init__.py
@@ -1,5 +1,7 @@
 """Init module for the library."""
 
+import os
+from ._version import get_versions
 from .LocalClassifierPerLevel import LocalClassifierPerLevel
 from .LocalClassifierPerNode import LocalClassifierPerNode
 from .LocalClassifierPerParentNode import LocalClassifierPerParentNode
@@ -7,6 +9,7 @@
 from .MultiLabelLocalClassifierPerParentNode import (
     MultiLabelLocalClassifierPerParentNode,
 )
+from .Explainer import Explainer
 from ._version import get_versions
 
 __version__ = get_versions()["version"]
@@ -16,6 +19,7 @@
     "LocalClassifierPerNode",
     "LocalClassifierPerParentNode",
     "LocalClassifierPerLevel",
+    "Explainer",
     "MultiLabelLocalClassifierPerNode",
     "MultiLabelLocalClassifierPerParentNode",
 ]
diff --git a/tests/test_Explainer.py b/tests/test_Explainer.py
new file mode 100644
index 00000000..ef1ea0da
--- /dev/null
+++ b/tests/test_Explainer.py
@@ -0,0 +1,153 @@
+import numpy as np
+import pytest
+from sklearn.ensemble import RandomForestClassifier
+from hiclass import (
+    LocalClassifierPerParentNode,
+    LocalClassifierPerNode,
+    LocalClassifierPerLevel,
+    Explainer,
+)
+
+try:
+    import shap
+except ImportError:
+    shap_installed = False
+else:
+    shap_installed = True
+
+classifiers = [
+    LocalClassifierPerLevel,
+    LocalClassifierPerParentNode,
+    LocalClassifierPerNode,
+]
+
+
+# TODO : Add parametrized tests
+
+
+@pytest.mark.skipif(not shap_installed, reason="shap not installed")
+@pytest.fixture
+def explainer_data():
+    np.random.seed(42)
+    x_train = np.random.randn(4, 3)
+    y_train = np.array(
+        [["a", "b", "d"], ["a", "b", "e"], ["a", "c", "f"], ["a", "c", "g"]]
+    )
+    x_test = np.random.randn(3, 3)
+
+    return x_train, x_test, y_train
+
+
+@pytest.mark.skipif(not shap_installed, reason="shap not installed")
+def test_explainer_tree(explainer_data):
+    rfc = RandomForestClassifier()
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=rfc, replace_classifiers=False
+    )
+
+    x_train, x_test, y_train = explainer_data
+
+    lcppn.fit(x_train, y_train)
+
+    explainer = Explainer(lcppn, data=x_train, mode="tree")
+    shap_dict = explainer.explain(x_test)
+    assert shap_dict is not None
+
+    # for key, val in shap_dict.items():
+    #     # Assert on shapes of shap values, must match (target_classes, num_samples, num_features)
+    #     model = lcppn.hierarchy_.nodes[key]["classifier"]
+    #     assert shap_dict[key].shape == (
+    #         len(model.classes_),
+    #         x_test.shape[0],
+    #         x_test.shape[1],
+    #     )
+
+
+@pytest.mark.skipif(not shap_installed, reason="shap not installed")
+def test_explainer_tree_traversal(explainer_data):
+    rfc = RandomForestClassifier()
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=rfc, replace_classifiers=False
+    )
+
+    x_train, x_test, y_train = explainer_data
+
+    lcppn.fit(x_train, y_train)
+
+    explainer = Explainer(lcppn, data=x_train, mode="tree")
+    shap_dict = explainer.explain(x_test)
+    assert shap_dict is not None
+    #
+    # for key, val in shap_dict.items():
+    #     # Assert on shapes of shap values, must match (target_classes, num_samples, num_features)
+    #     model = lcppn.hierarchy_.nodes[key]["classifier"]
+    #     assert shap_dict[key].shape == (
+    #         len(model.classes_),
+    #         x_test.shape[0],
+    #         x_test.shape[1],
+    #     )
+
+
+@pytest.fixture
+def explainer_data_no_root():
+    x_train = np.random.randn(6, 3)
+    y_train = np.array(
+        [
+            ["a", "b", "c"],
+            ["x", "y", "z"],
+            ["a", "b", "c"],
+            ["x", "y", "z"],
+            ["a", "b", "c"],
+            ["x", "y", "z"],
+        ]
+    )
+    x_test = np.random.randn(1, 3)
+    return x_train, x_test, y_train
+
+
+@pytest.mark.skipif(not shap_installed, reason="shap not installed")
+def test_explainer_tree_no_root(explainer_data_no_root):
+    rfc = RandomForestClassifier()
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=rfc, replace_classifiers=False
+    )
+
+    x_train, x_test, y_train = explainer_data_no_root
+
+    lcppn.fit(x_train, y_train)
+
+    lcppn.predict(x_test)
+    explainer = Explainer(lcppn, data=x_train, mode="tree")
+    shap_dict = explainer.explain(x_test)
+    assert shap_dict is not None
+
+    # for key, val in shap_dict.items():
+    #     # Assert on shapes of shap values, must match (target_classes, num_samples, num_features)
+    #     model = lcppn.hierarchy_.nodes[key]["classifier"]
+    #     assert shap_dict[key].shape == (
+    #         len(model.classes_),
+    #         x_test.shape[0],
+    #         x_test.shape[1],
+    #     )
+
+
+@pytest.mark.skipif(not shap_installed, reason="shap not installed")
+@pytest.mark.parametrize("data", ["explainer_data", "explainer_data_no_root"])
+def test_traversal_path(data, request):
+    x_train, x_test, y_train = request.getfixturevalue(data)
+    rfc = RandomForestClassifier()
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=rfc, replace_classifiers=False
+    )
+
+    lcppn.fit(x_train, y_train)
+    explainer = Explainer(lcppn, data=x_train, mode="tree")
+    traversals = explainer._get_traversed_nodes(x_test)
+    preds = lcppn.predict(x_test)
+    assert len(preds) == len(traversals)
+    for i in range(len(x_test)):
+        for j in range(len(traversals[i])):
+            if traversals[i][j] == lcppn.root_:
+                continue
+            label = traversals[i][j].split("::HiClass::Separator::")[-1]
+            assert label == preds[i][j - 1]
diff --git a/tests/test_LocalClassifierPerLevel.py b/tests/test_LocalClassifierPerLevel.py
index 27312f85..144d6d1f 100644
--- a/tests/test_LocalClassifierPerLevel.py
+++ b/tests/test_LocalClassifierPerLevel.py
@@ -7,9 +7,11 @@
 from scipy.sparse import csr_matrix
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 from hiclass import LocalClassifierPerLevel
+from hiclass.Explainer import Explainer
 
 
 @parametrize_with_checks([LocalClassifierPerLevel()])
diff --git a/tests/test_LocalClassifierPerNode.py b/tests/test_LocalClassifierPerNode.py
index 670c9823..cd134c10 100644
--- a/tests/test_LocalClassifierPerNode.py
+++ b/tests/test_LocalClassifierPerNode.py
@@ -7,12 +7,16 @@
 from scipy.sparse import csr_matrix
 from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 
 from hiclass import LocalClassifierPerNode
+
 from hiclass.BinaryPolicy import ExclusivePolicy
 
+from hiclass.Explainer import Explainer
+
 
 @parametrize_with_checks([LocalClassifierPerNode()])
 def test_sklearn_compatible_estimator(estimator, check):
diff --git a/tests/test_LocalClassifierPerParentNode.py b/tests/test_LocalClassifierPerParentNode.py
index 922a03a3..81b38556 100644
--- a/tests/test_LocalClassifierPerParentNode.py
+++ b/tests/test_LocalClassifierPerParentNode.py
@@ -10,7 +10,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
-
+from sklearn.ensemble import RandomForestClassifier
 from hiclass import LocalClassifierPerParentNode
 
 
@@ -184,3 +184,26 @@ def test_fit_predict():
     lcppn.fit(x, y)
     predictions = lcppn.predict(x)
     assert_array_equal(y, predictions)
+
+
+def test_predict_proba():
+    rfc = RandomForestClassifier()
+    lcppn = LocalClassifierPerParentNode(
+        local_classifier=rfc, replace_classifiers=False
+    )
+
+    x_train = np.random.randn(4, 3)
+    y_train = np.array(
+        [
+            ["a", "b", "d", "k"],
+            ["a", "b", "e", "l"],
+            ["a", "c", "f", "k"],
+            ["a", "c", "g", "l"],
+        ]
+    )
+    x_test = np.random.randn(1, 3)
+
+    lcppn.fit(x_train, y_train)
+
+    pred_proba_dict = lcppn.get_predict_proba(x_test)
+    assert pred_proba_dict is not None