diff --git a/docs/source/notebooks/usage.ipynb b/docs/source/notebooks/usage.ipynb
index 20e7e15..a97a245 100644
--- a/docs/source/notebooks/usage.ipynb
+++ b/docs/source/notebooks/usage.ipynb
@@ -4,54 +4,130 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Title (needed)"
+    "# Usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the library"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import tefs\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "plt.rc(\"text\", usetex=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate some random data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           x1        x2        x3\n",
-      "0    1.764052  0.555963 -1.532921\n",
-      "1    0.400157  0.892474 -1.711970\n",
-      "2    0.978738 -0.422315  0.046135\n",
-      "3    2.240893  0.104714 -0.958374\n",
-      "4    1.867558  0.228053 -0.080812\n",
-      "..        ...       ...       ...\n",
-      "995  0.412871  0.097751  2.079177\n",
-      "996 -0.198399  1.401523 -0.907466\n",
-      "997  0.094192  0.158434 -0.192404\n",
-      "998 -1.147611 -1.141901 -1.212516\n",
-      "999 -0.358114 -1.310970 -0.080599\n",
-      "\n",
-      "[1000 rows x 3 columns]\n",
-      "0      1.400443\n",
-      "1      1.424361\n",
-      "2      0.873649\n",
-      "3      2.523680\n",
-      "4      0.276468\n",
-      "         ...   \n",
-      "995    2.488424\n",
-      "996    1.042324\n",
-      "997    0.989404\n",
-      "998   -3.272610\n",
-      "999   -1.335277\n",
-      "Name: y, Length: 1000, dtype: float64\n",
-      "['x1', 'x2', 'x3']\n"
-     ]
+     "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>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>x3</th>\n",
+       "      <th>x4</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.764052</td>\n",
+       "      <td>0.555963</td>\n",
+       "      <td>-1.532921</td>\n",
+       "      <td>2.380368</td>\n",
+       "      <td>1.400443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.400157</td>\n",
+       "      <td>0.892474</td>\n",
+       "      <td>-1.711970</td>\n",
+       "      <td>0.149383</td>\n",
+       "      <td>1.424361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.978738</td>\n",
+       "      <td>-0.422315</td>\n",
+       "      <td>0.046135</td>\n",
+       "      <td>0.488071</td>\n",
+       "      <td>0.873649</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.240893</td>\n",
+       "      <td>0.104714</td>\n",
+       "      <td>-0.958374</td>\n",
+       "      <td>1.638863</td>\n",
+       "      <td>2.523680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.867558</td>\n",
+       "      <td>0.228053</td>\n",
+       "      <td>-0.080812</td>\n",
+       "      <td>0.803944</td>\n",
+       "      <td>0.276468</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         x1        x2        x3        x4         y\n",
+       "0  1.764052  0.555963 -1.532921  2.380368  1.400443\n",
+       "1  0.400157  0.892474 -1.711970  0.149383  1.424361\n",
+       "2  0.978738 -0.422315  0.046135  0.488071  0.873649\n",
+       "3  2.240893  0.104714 -0.958374  1.638863  2.523680\n",
+       "4  1.867558  0.228053 -0.080812  0.803944  0.276468"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "import tefs\n",
-    "\n",
-    "# Generate some random data\n",
-    "import numpy as np\n",
     "np.random.seed(0)\n",
     "n = 1000\n",
     "x1 = np.random.normal(size=n)\n",
@@ -61,26 +137,57 @@
     "y = x1 + x2 + x3 + np.random.normal(size=n)\n",
     "data = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3, 'x4': x4, 'y': y})\n",
     "\n",
-    "# Define features and target\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define features and target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "features = data[['x1', 'x2', 'x3']]\n",
     "target = data['y']\n",
-    "features_names = list(features.columns)\n",
-    "\n",
-    "# Set k using the rule of thumb\n",
+    "features_names = list(features.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "n_samples = len(data)\n",
-    "k = n_samples // 10\n",
+    "k = n_samples // 10 # Set k using the rule of thumb\n",
     "direction = 'forward' # or 'backward'\n",
     "lag_features = 1\n",
-    "lag_target = 1\n",
-    "\n",
-    "print(features)\n",
-    "print(target)\n",
-    "print(features_names)\n"
+    "lag_target = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform feature selection"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -94,9 +201,7 @@
     }
    ],
    "source": [
-    "\n",
-    "# Perform feature selection\n",
-    "result = tefs.fs(\n",
+    "results = tefs.fs(\n",
     "    features=features.values,\n",
     "    target=target.values,\n",
     "    k=k,\n",
@@ -108,6 +213,133 @@
     "    n_jobs=4,\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 449,
+       "width": 683
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "tefs.plot_te_results(results, features_names, ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select features based on threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selecting features with direction forward and threshold 0\n",
+      "Stopping condition reached: all feature scores are negative\n"
+     ]
+    }
+   ],
+   "source": [
+    "features_indexes = tefs.select_features(\n",
+    "    results=results,\n",
+    "    threshold=0,\n",
+    "    direction=direction,\n",
+    "    verbose=1,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_features = [features_names[i] for i in features_indexes]\n",
+    "selected_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select specific number of features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Selecting 2 features with direction forward\n"
+     ]
+    }
+   ],
+   "source": [
+    "features_indexes = tefs.select_n_features(\n",
+    "    results=results,\n",
+    "    n=2,\n",
+    "    direction=direction,\n",
+    "    verbose=1,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['x1', 'x2']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selected_features = [features_names[i] for i in features_indexes]\n",
+    "selected_features"
+   ]
   }
  ],
  "metadata": {