diff --git a/labs/xgboostLab.ipynb b/labs/xgboostLab.ipynb
index bd9de85..cd4cdc3 100644
--- a/labs/xgboostLab.ipynb
+++ b/labs/xgboostLab.ipynb
@@ -16,8 +16,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "8L5KyC6KP_yY",
-        "colab_type": "text"
+        "id": "8L5KyC6KP_yY"
       },
       "source": [
         "# XGBoost Lab\n",
@@ -249,7 +248,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "d3c2iMbCP9aq",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -372,8 +370,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "b8hWOON-0qEU",
-        "colab_type": "text"
+        "id": "b8hWOON-0qEU"
       },
       "source": [
         "And for our first prediction $f_0$ let's predict just the average value:\n"
@@ -383,7 +380,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "ddG9_pVPTwDW",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -512,8 +508,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ijAYdpZd2fzx",
-        "colab_type": "text"
+        "id": "ijAYdpZd2fzx"
       },
       "source": [
         "and the differences between our predictions and the actual values"
@@ -523,7 +518,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "CKG3OvcB1RWv",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -663,8 +657,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "JYYABnmp2niq",
-        "colab_type": "text"
+        "id": "JYYABnmp2niq"
       },
       "source": [
         "That $y-f_0$ is the residual. What is left, or how far the first classifier was off. The residual is what the second classifier is trying to predict.\n",
@@ -678,7 +671,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "OVqyVkYF2XSO",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -841,8 +833,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "T72unS4a7HJP",
-        "colab_type": "text"
+        "id": "T72unS4a7HJP"
       },
       "source": [
         "And the next classifier will try to predict $y-f_1$ based on x.\n",
@@ -867,7 +858,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "ma21zshP4umI",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 321
@@ -910,8 +900,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "U8XREkjWB5fS",
-        "colab_type": "text"
+        "id": "U8XREkjWB5fS"
       },
       "source": [
         "This reminds me of a section of the *Hitchhiker's Guide to the Galaxy* by Douglas Adams, where Marvin, the robot, is asked to bring two hitchhikers to the bridge and he says:\n",
@@ -932,7 +921,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "83Bp2hvE6bcg",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 360
@@ -975,8 +963,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "XZmJUO_2CGez",
-        "colab_type": "text"
+        "id": "XZmJUO_2CGez"
       },
       "source": [
         "It is a Tesla T4, which has 320 tensor cores.\n",
@@ -993,9 +980,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "UwUq3mqwB9EL",
-        "colab_type": "code",
-        "colab": {}
+        "id": "UwUq3mqwB9EL"
       },
       "source": [
         "import pandas as pd\n",
@@ -1015,8 +1000,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "pmHyfTQeCUQs",
-        "colab_type": "text"
+        "id": "pmHyfTQeCUQs"
       },
       "source": [
         "### Create an instance of the XGBoost classifier\n",
@@ -1027,7 +1011,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "acYGT2PzCRJV",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 154
@@ -1067,8 +1050,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "TX_yoP5kDKjl",
-        "colab_type": "text"
+        "id": "TX_yoP5kDKjl"
       },
       "source": [
         "Let's take a look at those parameters.\n",
@@ -1085,7 +1067,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "MBxXaHTVCbt0",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 154
@@ -1121,8 +1102,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "xekQss6bFp65",
-        "colab_type": "text"
+        "id": "xekQss6bFp65"
       },
       "source": [
         "### evaluate model\n",
@@ -1133,7 +1113,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "SO-caKnjFlG-",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -1165,8 +1144,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hoX-aaylF8He",
-        "colab_type": "text"
+        "id": "hoX-aaylF8He"
       },
       "source": [
         "We ran a state-of-the-art algorithm on a GPU. Yay us!\n",
@@ -1205,7 +1183,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "6O1qfbyAFvgW",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 554
@@ -1490,8 +1467,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "y-Iodm2UOrs7",
-        "colab_type": "text"
+        "id": "y-Iodm2UOrs7"
       },
       "source": [
         "## divide features and labels\n",
@@ -1502,7 +1478,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "8I01XNJzPOI2",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 417
@@ -1774,8 +1749,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "hRTPkOHzNITR",
-        "colab_type": "text"
+        "id": "hRTPkOHzNITR"
       },
       "source": [
         "Now let's one hot encode the features using sklearn's OneHotEncoder.\n"
@@ -1785,7 +1759,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "JCItEsf8K2TZ",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
@@ -1819,8 +1792,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "QZyAn3WLZMqV",
-        "colab_type": "text"
+        "id": "QZyAn3WLZMqV"
       },
       "source": [
         "Fantastic!  \n",
@@ -1833,7 +1805,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "pc-AXr9FZGhW",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
@@ -1865,8 +1836,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "fRDTurCqa9nq",
-        "colab_type": "text"
+        "id": "fRDTurCqa9nq"
       },
       "source": [
         "You may have noticed that we put a whopping 70% of the data in the test set. We did this because when we are just playing with things to gain an understanding we don't want to wait hours for a result.\n",
@@ -1880,9 +1850,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "1Py0OOb5aBy3",
-        "colab_type": "code",
-        "colab": {}
+        "id": "1Py0OOb5aBy3"
       },
       "source": [
         "## TO DO call it model\n"
@@ -1893,8 +1861,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "eks7EiOebgkj",
-        "colab_type": "text"
+        "id": "eks7EiOebgkj"
       },
       "source": [
         "Now let's say we want to find the best hyperparameter values for \n",
@@ -1909,9 +1876,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "JkqwXndrbfhP",
-        "colab_type": "code",
-        "colab": {}
+        "id": "JkqwXndrbfhP"
       },
       "source": [
         "# TODO\n",
@@ -1923,8 +1888,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "b4GoFt4hc4zt",
-        "colab_type": "text"
+        "id": "b4GoFt4hc4zt"
       },
       "source": [
         "\n",
@@ -1937,9 +1901,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "gZkjYTZHc1iP",
-        "colab_type": "code",
-        "colab": {}
+        "id": "gZkjYTZHc1iP"
       },
       "source": [
         "# TO DO\n",
@@ -1957,8 +1919,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "3syNvBizdMYj",
-        "colab_type": "text"
+        "id": "3syNvBizdMYj"
       },
       "source": [
         "Let's fit the model (this will take awhile)"
@@ -1968,7 +1929,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "BVxaKf8OdK9n",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 157
@@ -2011,8 +1971,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "lhJmfE6gM6IJ",
-        "colab_type": "text"
+        "id": "lhJmfE6gM6IJ"
       },
       "source": [
         "Now let's see what the best parameters are, make predictions on our test data, and check accuracy..."
@@ -2022,7 +1981,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "CFPTUpq_dT0s",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -2051,9 +2009,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "PTboDXG8xsRV",
-        "colab_type": "code",
-        "colab": {}
+        "id": "PTboDXG8xsRV"
       },
       "source": [
         "predictions = random_search.best_estimator_.predict(adult_test_features)"
@@ -2065,7 +2021,6 @@
       "cell_type": "code",
       "metadata": {
         "id": "dA0tfa2n7t2w",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -2094,8 +2049,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "zmC50ToeLn7S",
-        "colab_type": "text"
+        "id": "zmC50ToeLn7S"
       },
       "source": [
         "### This ends our first look at XGBoost\n",
@@ -2105,9 +2059,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "UTPwWCmh7y8k",
-        "colab_type": "code",
-        "colab": {}
+        "id": "UTPwWCmh7y8k"
       },
       "source": [
         ""