diff --git a/docs/tutorials/nb_shap_feature_elimination.ipynb b/docs/tutorials/nb_shap_feature_elimination.ipynb
index 14eb6574..a68d4ad2 100644
--- a/docs/tutorials/nb_shap_feature_elimination.ipynb
+++ b/docs/tutorials/nb_shap_feature_elimination.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# ShapRFECV - Recursive Feature Elimination using SHAP importance\n",
     "\n",
@@ -42,24 +41,23 @@
     "- 1 static feature\n",
     "- 1 static feature\n",
     "- 1 feature with missing values"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "%%capture\n",
     "!pip install probatus\n",
     "!pip install lightgbm"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
    "source": [
     "from probatus.feature_elimination import ShapRFECV\n",
     "from sklearn.datasets import make_classification\n",
@@ -78,14 +76,20 @@
     "\n",
     "X['f2_missing'] = X['f2_missing'].apply(lambda x: x if np.random.rand()<0.8 else np.nan)\n",
     "X['f3_static'] = 0"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 3,
+   "source": [
+    "#First 5 rows of first 5 columns\n",
+    "X[feature_names[:5]].head()"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/html": [
        "<div>\n",
@@ -117,7 +121,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>3.399287</td>\n",
-       "      <td>-3.902230</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>0.037207</td>\n",
        "      <td>-0.211075</td>\n",
@@ -133,7 +137,7 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-0.690014</td>\n",
-       "      <td>1.350847</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>1.837895</td>\n",
        "      <td>-0.745689</td>\n",
@@ -160,26 +164,21 @@
       ],
       "text/plain": [
        "         f1  f2_missing  f3_static        f4        f5\n",
-       "0  3.399287   -3.902230          0  0.037207 -0.211075\n",
+       "0  3.399287         NaN          0  0.037207 -0.211075\n",
        "1 -2.480698    0.772855          0  0.302824  0.729950\n",
-       "2 -0.690014    1.350847          0  1.837895 -0.745689\n",
+       "2 -0.690014         NaN          0  1.837895 -0.745689\n",
        "3 -5.291164    4.559465          0 -1.277930  3.688404\n",
        "4 -1.028435    1.505766          0 -0.576209 -0.790525"
       ]
      },
-     "execution_count": 2,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 3
     }
    ],
-   "source": [
-    "#First 5 rows of first 5 columns\n",
-    "X[feature_names[:5]].head()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Set up the model and model tuning\n",
     "\n",
@@ -187,15 +186,12 @@
     "We recommend using [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html), which by default handles missing values and categorical features.\n",
     "\n",
     "The example below applies randomized search in order to optimize the hyperparameters of the model at each iteration of the search."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "execution_count": 4,
    "source": [
     "clf = lightgbm.LGBMClassifier(max_depth=5, class_weight='balanced')\n",
     "\n",
@@ -204,41 +200,49 @@
     "    'num_leaves': [3, 5, 7, 10],\n",
     "}\n",
     "search = RandomizedSearchCV(clf, param_grid)\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {
+    "scrolled": false
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Apply ShapRFECV\n",
     "\n",
     "Now let's apply the [ShapRFECV](https://ing-bank.github.io/probatus/api/feature_elimination.html)."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
    "source": [
     "shap_elimination = ShapRFECV(\n",
     "    clf=search, step=0.2, cv=10, scoring='roc_auc', n_jobs=3)\n",
     "report = shap_elimination.fit_compute(X, y)"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "At the end of the process, you can investigate the results for each iteration."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 6,
+   "source": [
+    "#First 5 rows of first 5 columns\n",
+    "report[['num_features', 'features_set', 'val_metric_mean']]"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/html": [
        "<div>\n",
@@ -274,67 +278,67 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>16</td>\n",
-       "      <td>[f17, f19, f20, f8, f1, f9, f4, f18, f16, f14,...</td>\n",
-       "      <td>0.922</td>\n",
+       "      <td>[f14, f4, f18, f1, f11, f12, f20, f16, f10, f1...</td>\n",
+       "      <td>0.923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>13</td>\n",
-       "      <td>[f20, f19, f8, f1, f9, f18, f16, f14, f11, f5,...</td>\n",
+       "      <td>[f14, f18, f1, f11, f12, f20, f16, f10, f15, f...</td>\n",
        "      <td>0.923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>11</td>\n",
-       "      <td>[f18, f20, f19, f16, f8, f14, f11, f5, f9, f10...</td>\n",
-       "      <td>0.923</td>\n",
+       "      <td>[f10, f14, f18, f15, f11, f8, f20, f9, f5, f16...</td>\n",
+       "      <td>0.904</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>9</td>\n",
-       "      <td>[f20, f19, f16, f8, f14, f11, f5, f9, f15]</td>\n",
-       "      <td>0.904</td>\n",
+       "      <td>[f14, f15, f9, f8, f11, f20, f5, f16, f19]</td>\n",
+       "      <td>0.932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>8</td>\n",
-       "      <td>[f20, f19, f16, f8, f14, f5, f9, f15]</td>\n",
+       "      <td>[f14, f15, f9, f8, f20, f5, f16, f19]</td>\n",
        "      <td>0.910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>7</td>\n",
-       "      <td>[f20, f19, f16, f8, f14, f9, f15]</td>\n",
-       "      <td>0.905</td>\n",
+       "      <td>[f14, f15, f9, f8, f20, f16, f19]</td>\n",
+       "      <td>0.921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>6</td>\n",
-       "      <td>[f20, f19, f16, f14, f9, f15]</td>\n",
-       "      <td>0.917</td>\n",
+       "      <td>[f14, f15, f9, f20, f16, f19]</td>\n",
+       "      <td>0.916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>5</td>\n",
-       "      <td>[f19, f16, f14, f9, f15]</td>\n",
+       "      <td>[f14, f15, f9, f16, f19]</td>\n",
        "      <td>0.889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>4</td>\n",
-       "      <td>[f19, f9, f16, f14]</td>\n",
-       "      <td>0.877</td>\n",
+       "      <td>[f16, f19, f14, f9]</td>\n",
+       "      <td>0.879</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>3</td>\n",
-       "      <td>[f19, f16, f9]</td>\n",
+       "      <td>[f16, f19, f9]</td>\n",
        "      <td>0.867</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>2</td>\n",
-       "      <td>[f19, f16]</td>\n",
+       "      <td>[f16, f19]</td>\n",
        "      <td>0.818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -350,1680 +354,195 @@
       "text/plain": [
        "    num_features                                       features_set  \\\n",
        "1             20  [f1, f2_missing, f3_static, f4, f5, f6, f7, f8...   \n",
-       "2             16  [f17, f19, f20, f8, f1, f9, f4, f18, f16, f14,...   \n",
-       "3             13  [f20, f19, f8, f1, f9, f18, f16, f14, f11, f5,...   \n",
-       "4             11  [f18, f20, f19, f16, f8, f14, f11, f5, f9, f10...   \n",
-       "5              9         [f20, f19, f16, f8, f14, f11, f5, f9, f15]   \n",
-       "6              8              [f20, f19, f16, f8, f14, f5, f9, f15]   \n",
-       "7              7                  [f20, f19, f16, f8, f14, f9, f15]   \n",
-       "8              6                      [f20, f19, f16, f14, f9, f15]   \n",
-       "9              5                           [f19, f16, f14, f9, f15]   \n",
-       "10             4                                [f19, f9, f16, f14]   \n",
-       "11             3                                     [f19, f16, f9]   \n",
-       "12             2                                         [f19, f16]   \n",
+       "2             16  [f14, f4, f18, f1, f11, f12, f20, f16, f10, f1...   \n",
+       "3             13  [f14, f18, f1, f11, f12, f20, f16, f10, f15, f...   \n",
+       "4             11  [f10, f14, f18, f15, f11, f8, f20, f9, f5, f16...   \n",
+       "5              9         [f14, f15, f9, f8, f11, f20, f5, f16, f19]   \n",
+       "6              8              [f14, f15, f9, f8, f20, f5, f16, f19]   \n",
+       "7              7                  [f14, f15, f9, f8, f20, f16, f19]   \n",
+       "8              6                      [f14, f15, f9, f20, f16, f19]   \n",
+       "9              5                           [f14, f15, f9, f16, f19]   \n",
+       "10             4                                [f16, f19, f14, f9]   \n",
+       "11             3                                     [f16, f19, f9]   \n",
+       "12             2                                         [f16, f19]   \n",
        "13             1                                              [f16]   \n",
        "\n",
        "    val_metric_mean  \n",
        "1             0.922  \n",
-       "2             0.922  \n",
+       "2             0.923  \n",
        "3             0.923  \n",
-       "4             0.923  \n",
-       "5             0.904  \n",
+       "4             0.904  \n",
+       "5             0.932  \n",
        "6             0.910  \n",
-       "7             0.905  \n",
-       "8             0.917  \n",
+       "7             0.921  \n",
+       "8             0.916  \n",
        "9             0.889  \n",
-       "10            0.877  \n",
+       "10            0.879  \n",
        "11            0.867  \n",
        "12            0.818  \n",
        "13            0.720  "
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
-   "source": [
-    "#First 5 rows of first 5 columns\n",
-    "report[['num_features', 'features_set', 'val_metric_mean']]"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "###### Once the process is completed, you can visualize the results. \n",
     "\n",
-    "Let's investigate the performance plot. In this case, the Validation AUC score has the highest Validation AUC at 11 features and a peak at 6 features."
-   ]
+    "Let's investigate the performance plot. In this case, the Validation AUC score has the highest Validation AUC at 9 features and a peak at 7 features."
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 8,
+   "source": [
+    "performance_plot = shap_elimination.plot()"
+   ],
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
-   "source": [
-    "performance_plot = shap_elimination.plot()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Let's see the final feature set:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 9,
+   "source": [
+    "shap_elimination.get_reduced_features_set(num_features=7)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "['f20', 'f19', 'f16', 'f14', 'f9', 'f15']"
+       "['f14', 'f15', 'f9', 'f8', 'f20', 'f16', 'f19']"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 9
     }
    ],
-   "source": [
-    "shap_elimination.get_reduced_features_set(num_features=6)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "You can also provide a list of features that should not be eliminated. \n",
-    "Say based on your prior knowledge you know that the features `f10,f19,f15` are important and should not be eliminated. This can be done by providing a list of columns to `columns_to_keep` parameter in the `fit()` function.\n"
-   ]
+    "Let's see the final feature set absolute shap values:"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 10,
+   "source": [
+    "shap_elimination.get_reduced_features_set_shap_values(num_features=7)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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-       "     </g>\n",
-       "    </g>\n",
-       "   </g>\n",
-       "  </g>\n",
-       " </g>\n",
-       " <defs>\n",
-       "  <clipPath id=\"pc3abcc006f\">\n",
-       "   <rect height=\"217.44\" width=\"334.8\" x=\"50.421875\" y=\"22.318125\"/>\n",
-       "  </clipPath>\n",
-       " </defs>\n",
-       "</svg>\n"
-      ],
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "{'f16': 0.37219772552629,\n",
+       " 'f19': 0.3042499853571644,\n",
+       " 'f9': 0.2027409748599166,\n",
+       " 'f15': 0.14422338254842906,\n",
+       " 'f14': 0.10493848810181954,\n",
+       " 'f20': 0.09953636616375297,\n",
+       " 'f8': 0.08929012012551539}"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "metadata": {},
+     "execution_count": 10
     }
    ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "You can also provide a list of features that should not be eliminated. \n",
+    "Say based on your prior knowledge you know that the features `f10,f19,f15` are important and should not be eliminated. This can be done by providing a list of columns to `columns_to_keep` parameter in the `fit()` function.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "source": [
     "shap_elimination = ShapRFECV(\n",
     "    clf=search, step=0.2, cv=10, scoring='roc_auc', n_jobs=3  ,min_features_to_select=4)\n",
     "report = shap_elimination.fit_compute(X, y, columns_to_keep=['f10','f15','f19'])\n",
     "\n",
     "performance_plot = shap_elimination.plot()\n"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Let's see the final feature set:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 12,
+   "source": [
+    "shap_elimination.get_reduced_features_set(num_features=4)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "['f15', 'f16', 'f10', 'f19']"
+       "['f16', 'f19', 'f15', 'f10']"
       ]
      },
-     "execution_count": 10,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 12
     }
    ],
-   "source": [
-    "shap_elimination.get_reduced_features_set(num_features=4)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## EarlyStoppingShapRFECV\n",
     "\n",
@@ -2032,13 +551,12 @@
     "Early stopping is thus a way of mitigating overfitting in a relatively cheaply, without having to find the ideal regularization hyperparameters. It is particularly useful for handling large datasets, since it reduces the number of training steps which can decrease the modelling time.\n",
     "\n",
     "`EarlyStoppingShapRFECV` is a child of `ShapRFECV` with limited support for early stopping and the example below shows how to use it with LightGBM."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": null,
    "source": [
     "from probatus.feature_elimination import EarlyStoppingShapRFECV\n",
     "\n",
@@ -2048,1549 +566,50 @@
     "shap_elimination = EarlyStoppingShapRFECV(\n",
     "    clf=search, step=0.2, cv=10, scoring='roc_auc', eval_metric='auc', early_stopping_rounds=5, n_jobs=3)\n",
     "report = shap_elimination.fit_compute(X, y)"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 14,
+   "source": [
+    "# Make plots\n",
+    "performance_plot = shap_elimination.plot()\n",
+    "\n",
+    "# Get final feature set\n",
+    "final_features_set = shap_elimination.get_reduced_features_set(num_features=9)"
+   ],
    "outputs": [
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
-   "source": [
-    "# Make plots\n",
-    "performance_plot = shap_elimination.plot()\n",
-    "\n",
-    "# Get final feature set\n",
-    "final_features_set = shap_elimination.get_reduced_features_set(num_features=9)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "As it is hinted in the example above, with large datasets and simple base learners, early stopping can be a much faster alternative to hyperparameter optimization of the ideal number of trees.\n",
     "\n",
     "Note that although `EarlyStoppingShapRFECV` supports hyperparameter search models as input, early stopping is used only during the Shapley value estimation step, and not during hyperparameter search. For this reason, _if you are not using early stopping, you should use the parent class, `ShapRFECV`, instead of `EarlyStoppingShapRFECV`_."
-   ]
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "name": "python3",
+   "display_name": "Python 3.7.10 64-bit ('probatus_env': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3602,9 +621,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.10"
+  },
+  "interpreter": {
+   "hash": "5f76485cfc9cf16bdd565fed88f4b47608cce394e96187a7755e8179d7fb5123"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/probatus/feature_elimination/feature_elimination.py b/probatus/feature_elimination/feature_elimination.py
index fd42c0ad..8e5c2844 100644
--- a/probatus/feature_elimination/feature_elimination.py
+++ b/probatus/feature_elimination/feature_elimination.py
@@ -292,6 +292,8 @@ def _report_current_results(
         train_metric_std,
         val_metric_mean,
         val_metric_std,
+        features_mean_abs_shap_value,
+        features_mean_shap_value,
     ):
         """
         This function adds the results from a current iteration to the report.
@@ -317,6 +319,12 @@ def _report_current_results(
 
             val_metric_std (float or int):
                 Std scoring metric measured on validation set during CV.
+
+            features_mean_abs_shap_value (dict of float):
+                Mean absolute shap values of features.
+
+            features_mean_shap_value (dict of float):
+                Mean shap values of features.
         """
 
         current_results = {
@@ -327,6 +335,8 @@ def _report_current_results(
             "train_metric_std": train_metric_std,
             "val_metric_mean": val_metric_mean,
             "val_metric_std": val_metric_std,
+            "features_mean_abs_shap_value": [features_mean_abs_shap_value],
+            "features_mean_shap_value": [features_mean_shap_value],
         }
 
         current_row = pd.DataFrame(current_results, index=[round_number])
@@ -563,6 +573,8 @@ def fit(self, X, y, sample_weight=None, columns_to_keep=None, column_names=None,
                 train_metric_std=np.round(np.std(scores_train), 3),
                 val_metric_mean=np.round(np.mean(scores_val), 3),
                 val_metric_std=np.round(np.std(scores_val), 3),
+                features_mean_abs_shap_value=shap_importance_df.to_dict()["mean_abs_shap_value"],
+                features_mean_shap_value=shap_importance_df.to_dict()["mean_shap_value"],
             )
             if self.verbose > 50:
                 print(
@@ -673,6 +685,37 @@ def get_reduced_features_set(self, num_features):
         else:
             return self.report_df[self.report_df.num_features == num_features]["features_set"].values[0]
 
+    def get_reduced_features_set_shap_values(self, num_features, abs_shap_values=True):
+        """
+        Gets the shap values of a features set after the feature elimination process, for a given number of features.
+
+        Args:
+            num_features (int):
+                Number of features in the reduced features set.
+
+            abs_shap_values (bool, optional):
+                If True, the absolute shap values are returned, otherwise the true shap values are.
+
+        Returns:
+            (dict of float):
+                (Absolute) shap values for the reduced features set.
+        """
+        self._check_if_fitted()
+
+        if num_features not in self.report_df.num_features.tolist():
+            raise (
+                ValueError(
+                    f"The provided number of features has not been achieved at any stage of the process. "
+                    f"You can select one of the following: {self.report_df.num_features.tolist()}"
+                )
+            )
+        else:
+            if abs_shap_values:
+                shap_values_col = "features_mean_abs_shap_value"
+            else:
+                shap_values_col = "features_mean_shap_value"
+            return self.report_df[self.report_df.num_features == num_features][shap_values_col].values[0]
+
     def plot(self, show=True, **figure_kwargs):
         """
         Generates plot of the model performance for each iteration of feature elimination.