From 3456aedd0fdbd1c9b037f8217ee7cab641930d23 Mon Sep 17 00:00:00 2001
From: Byeong-Hyeok Yu <52292818+osgeokr@users.noreply.github.com>
Date: Tue, 6 Feb 2024 00:27:26 +0900
Subject: [PATCH] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5=ED=95=B4=20?=
 =?UTF-8?q?=EC=83=9D=EC=84=B1=EB=90=A8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 .../species-distribution-modeling.ipynb       | 2487 ++++++++---------
 1 file changed, 1173 insertions(+), 1314 deletions(-)

diff --git a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb
index a29c7cdf4..f13aca137 100644
--- a/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb
+++ b/tutorials/species-distribution-modeling/species-distribution-modeling.ipynb
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             "inertia": true,
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@@ -9635,16 +9583,16 @@
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             "layers": [
-              "IPY_MODEL_053f3088b4ad436da16f8269e5d07b2e",
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-              "IPY_MODEL_1501f246f07a4d91a19667e1090b1f73"
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+              "IPY_MODEL_36088b2ad89d403997bdba095418da88",
+              "IPY_MODEL_ad7a8631b42142c2aaab712451dbbf21"
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-            "layout": "IPY_MODEL_fd25e9570f374653ae9468209fe3a54b",
-            "left": 6373,
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             "min_zoom": null,
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-            "north": 40.1452892956766,
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               "box_zoom",
@@ -9675,24 +9623,24 @@
             ],
             "panes": {},
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-            "right": 7173,
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             "scroll_wheel_zoom": true,
-            "south": 25.443274612305746,
-            "style": "IPY_MODEL_dee5114c449f458a889ede5f5447d693",
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             "tap": true,
             "tap_tolerance": 15,
-            "top": 3097,
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-            "west": 100.06347656250001,
+            "west": 118.10302734375001,
             "window_url": "https://sgoquz0z607-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20240201-060115_RC00_603318229",
             "world_copy_jump": false,
-            "zoom": 5,
+            "zoom": 6,
             "zoom_animation_threshold": 4,
             "zoom_delta": 1,
             "zoom_snap": 1
           }
         },
-        "1136a62f368c488dbd989bbe7aafe6e1": {
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           "model_module_version": "^0.18",
@@ -9714,10 +9662,10 @@
             ],
             "position": "topleft",
             "transparent_bg": false,
-            "widget": "IPY_MODEL_1760d31416424e489022ad2f9d2358e7"
+            "widget": "IPY_MODEL_b27fb9823e194bdab0197c64c24d8270"
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-        "0120edb48cec4e768fb2e7a0590f2c8b": {
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           "model_name": "LeafletZoomControlModel",
           "model_module_version": "^0.18",
@@ -9743,7 +9691,7 @@
             "zoom_out_title": "Zoom out"
           }
         },
-        "3b95748faa7a4f5c9b97d0533e14f609": {
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           "model_name": "LeafletFullScreenControlModel",
           "model_module_version": "^0.18",
@@ -9761,7 +9709,7 @@
             "position": "topleft"
           }
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-        "e6069552718849fd8098062260ac9dfb": {
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           "model_module": "jupyter-leaflet",
           "model_name": "LeafletDrawControlModel",
           "model_module_version": "^0.18",
@@ -9800,7 +9748,7 @@
             "remove": true
           }
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           "model_name": "LeafletScaleControlModel",
           "model_module_version": "^0.18",
@@ -9826,7 +9774,7 @@
             "update_when_idle": false
           }
         },
-        "40be0705f15f431e89c083e01eab94f9": {
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@@ -9867,7 +9815,7 @@
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           "model_name": "LeafletWidgetControlModel",
           "model_module_version": "^0.18",
@@ -9889,10 +9837,10 @@
             ],
             "position": "topright",
             "transparent_bg": false,
-            "widget": "IPY_MODEL_137d487ae6d64157b96becf8e2934a84"
+            "widget": "IPY_MODEL_b7fe85d1519349a5837c0e353cd68771"
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-        "08355bb00ea045fc945ee47ae4adfa7a": {
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           "model_name": "LeafletAttributionControlModel",
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@@ -9912,7 +9860,7 @@
             "prefix": "ipyleaflet"
           }
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           "model_module": "jupyter-leaflet",
           "model_name": "LeafletWidgetControlModel",
           "model_module_version": "^0.18",
@@ -9934,10 +9882,10 @@
             ],
             "position": "bottomright",
             "transparent_bg": false,
-            "widget": "IPY_MODEL_ce79d4180d314deaac48f63581a328c9"
+            "widget": "IPY_MODEL_3983d97bef154958b9bed5df4760d133"
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-        "dee5114c449f458a889ede5f5447d693": {
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           "model_module": "jupyter-leaflet",
           "model_name": "LeafletMapStyleModel",
           "model_module_version": "^0.18",
@@ -9952,7 +9900,7 @@
             "cursor": "grab"
           }
         },
-        "64618ee8d9fb4ec6ada73fe6a03b9a71": {
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           "model_module": "jupyter-leaflet",
           "model_name": "LeafletMapStyleModel",
           "model_module_version": "^0.18",
@@ -9967,7 +9915,7 @@
             "cursor": "move"
           }
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-        "053f3088b4ad436da16f8269e5d07b2e": {
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           "model_module": "jupyter-leaflet",
           "model_name": "LeafletTileLayerModel",
           "model_module_version": "^0.18",
@@ -10019,7 +9967,7 @@
             "zoom_offset": 0
           }
         },
-        "e5b7afbfdbc34e02b6d6908696a431b4": {
+        "36088b2ad89d403997bdba095418da88": {
           "model_module": "jupyter-leaflet",
           "model_name": "LeafletTileLayerModel",
           "model_module_version": "^0.18",
@@ -10066,12 +10014,12 @@
             "subitems": [],
             "tile_size": 256,
             "tms": false,
-            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/fc71a429733131108c53b6fe14eccb20-00cbcfbeabfea8c4cb7c78562e5671d3/tiles/{z}/{x}/{y}",
+            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/cb5a0accf78958fe8ee42b834ff97a51-b2ddae629cf5a4c67fb633b331beadc9/tiles/{z}/{x}/{y}",
             "visible": true,
             "zoom_offset": 0
           }
         },
-        "1501f246f07a4d91a19667e1090b1f73": {
+        "ad7a8631b42142c2aaab712451dbbf21": {
           "model_module": "jupyter-leaflet",
           "model_name": "LeafletTileLayerModel",
           "model_module_version": "^0.18",
@@ -10118,12 +10066,12 @@
             "subitems": [],
             "tile_size": 256,
             "tms": false,
-            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-cf30347e31a8547d2a571efa6d03d7f3/tiles/{z}/{x}/{y}",
+            "url": "https://earthengine.googleapis.com/v1/projects/ee-foss4g/maps/865398232320376ad0f518f1323209e5-73296bd0dc24a5fb2e3fb65a5a03f65a/tiles/{z}/{x}/{y}",
             "visible": true,
             "zoom_offset": 0
           }
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-        "fd25e9570f374653ae9468209fe3a54b": {
+        "33ba445201c8466092fe1bfaa03d66ce": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10175,7 +10123,7 @@
             "width": "800px"
           }
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-        "1b4d178364814408b78e608f67fcc078": {
+        "0bfcff1d63ab4e39b5d4807d9054dd70": {
           "model_module": "jupyter-leaflet",
           "model_name": "LeafletMapStyleModel",
           "model_module_version": "^0.18",
@@ -10190,7 +10138,7 @@
             "cursor": "grab"
           }
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-        "1760d31416424e489022ad2f9d2358e7": {
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           "model_module": "@jupyter-widgets/controls",
           "model_name": "HBoxModel",
           "model_module_version": "1.5.0",
@@ -10207,12 +10155,12 @@
             "_view_name": "HBoxView",
             "box_style": "",
             "children": [
-              "IPY_MODEL_d1bcf6455dfe466098bae22d88db7a40"
+              "IPY_MODEL_84de4feaa968477f80e33d73dc99bce6"
             ],
-            "layout": "IPY_MODEL_d1f00923fa374f21b0d0f85337e48433"
+            "layout": "IPY_MODEL_90c1dbad942044b8a5ca4ef8d81bd099"
           }
         },
-        "137d487ae6d64157b96becf8e2934a84": {
+        "b7fe85d1519349a5837c0e353cd68771": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "VBoxModel",
           "model_module_version": "1.5.0",
@@ -10229,12 +10177,12 @@
             "_view_name": "VBoxView",
             "box_style": "",
             "children": [
-              "IPY_MODEL_5bdea2b1052e4e3899b3cc5ccb05ebda"
+              "IPY_MODEL_467b9855bc8c4aa28f6349957b77d984"
             ],
-            "layout": "IPY_MODEL_9f5f2770e9e145dca7d403b8e3d2deaf"
+            "layout": "IPY_MODEL_8e55852b68324cd9981639250e21677e"
           }
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-        "ce79d4180d314deaac48f63581a328c9": {
+        "3983d97bef154958b9bed5df4760d133": {
           "model_module": "@jupyter-widgets/output",
           "model_name": "OutputModel",
           "model_module_version": "1.0.0",
@@ -10249,7 +10197,7 @@
             "_view_module": "@jupyter-widgets/output",
             "_view_module_version": "1.0.0",
             "_view_name": "OutputView",
-            "layout": "IPY_MODEL_1c15810c3b664092bab387df504d9211",
+            "layout": "IPY_MODEL_9a5769371ae94e74aafbae4296637b79",
             "msg_id": "",
             "outputs": [
               {
@@ -10263,7 +10211,7 @@
             ]
           }
         },
-        "d1bcf6455dfe466098bae22d88db7a40": {
+        "84de4feaa968477f80e33d73dc99bce6": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ToggleButtonModel",
           "model_module_version": "1.5.0",
@@ -10281,13 +10229,13 @@
             "description_tooltip": null,
             "disabled": false,
             "icon": "globe",
-            "layout": "IPY_MODEL_53c2802cbe654c00bdefc54c1828299d",
-            "style": "IPY_MODEL_013047cffc06425e912d3623696c2e65",
+            "layout": "IPY_MODEL_a507514f7c8c4c2993cc6df6b8b2b98c",
+            "style": "IPY_MODEL_76a573d962fb426abc03edba4717ee8f",
             "tooltip": "Search location/data",
             "value": false
           }
         },
-        "d1f00923fa374f21b0d0f85337e48433": {
+        "90c1dbad942044b8a5ca4ef8d81bd099": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10339,7 +10287,7 @@
             "width": null
           }
         },
-        "5bdea2b1052e4e3899b3cc5ccb05ebda": {
+        "467b9855bc8c4aa28f6349957b77d984": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "ToggleButtonModel",
           "model_module_version": "1.5.0",
@@ -10357,13 +10305,13 @@
             "description_tooltip": null,
             "disabled": false,
             "icon": "wrench",
-            "layout": "IPY_MODEL_a9a30c1d7ce2494ea74f1cfbb01c355f",
-            "style": "IPY_MODEL_f3684f852fe34e2fa56e21e365ab70eb",
+            "layout": "IPY_MODEL_624a8bb1365341ceaf7f3dc3a02c2bcd",
+            "style": "IPY_MODEL_1a430d25cfac48d3b6e37c886c2503d6",
             "tooltip": "Toolbar",
             "value": false
           }
         },
-        "9f5f2770e9e145dca7d403b8e3d2deaf": {
+        "8e55852b68324cd9981639250e21677e": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10415,7 +10363,7 @@
             "width": null
           }
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-        "1c15810c3b664092bab387df504d9211": {
+        "9a5769371ae94e74aafbae4296637b79": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10467,7 +10415,7 @@
             "width": "270px"
           }
         },
-        "53c2802cbe654c00bdefc54c1828299d": {
+        "a507514f7c8c4c2993cc6df6b8b2b98c": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10519,7 +10467,7 @@
             "width": "28px"
           }
         },
-        "013047cffc06425e912d3623696c2e65": {
+        "76a573d962fb426abc03edba4717ee8f": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",
@@ -10534,7 +10482,7 @@
             "description_width": ""
           }
         },
-        "a9a30c1d7ce2494ea74f1cfbb01c355f": {
+        "624a8bb1365341ceaf7f3dc3a02c2bcd": {
           "model_module": "@jupyter-widgets/base",
           "model_name": "LayoutModel",
           "model_module_version": "1.2.0",
@@ -10586,7 +10534,7 @@
             "width": "28px"
           }
         },
-        "f3684f852fe34e2fa56e21e365ab70eb": {
+        "1a430d25cfac48d3b6e37c886c2503d6": {
           "model_module": "@jupyter-widgets/controls",
           "model_name": "DescriptionStyleModel",
           "model_module_version": "1.5.0",
@@ -10608,12 +10556,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "8kdsGkYJXXKc",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 17
-        },
-        "outputId": "ea49ceb7-2fdf-4929-a5bf-a3bd2536fe8f"
+        "id": "8kdsGkYJXXKc"
       },
       "source": [
         "#@title Copyright 2023 The Earth Engine Community Authors { display-mode: \"form\" }\n",
@@ -10630,43 +10573,8 @@
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
       ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "            <style>\n",
-              "                .geemap-dark {\n",
-              "                    --jp-widgets-color: white;\n",
-              "                    --jp-widgets-label-color: white;\n",
-              "                    --jp-ui-font-color1: white;\n",
-              "                    --jp-layout-color2: #454545;\n",
-              "                    background-color: #383838;\n",
-              "                }\n",
-              "                    \n",
-              "                .geemap-dark .jupyter-button {\n",
-              "                    --jp-layout-color3: #383838;\n",
-              "                }\n",
-              "                \n",
-              "                .geemap-colab {\n",
-              "                    background-color: var(--colab-primary-surface-color, white);\n",
-              "                }\n",
-              "                    \n",
-              "                .geemap-colab .jupyter-button {\n",
-              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-              "                }\n",
-              "            </style>\n",
-              "            "
-            ]
-          },
-          "metadata": {}
-        }
-      ]
+      "execution_count": 1,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -10703,10 +10611,9 @@
         "ee.Authenticate()\n",
         "\n",
         "# Initialize the library.\n",
-        "# ee.Initialize(project='my-project')\n",
-        "ee.Initialize(project='ee-foss4g')"
+        "ee.Initialize(project='my-project')"
       ],
-      "execution_count": 1,
+      "execution_count": 2,
       "outputs": []
     },
     {
@@ -10787,50 +10694,10 @@
         "from statsmodels.stats.outliers_influence import variance_inflation_factor"
       ],
       "metadata": {
-        "id": "4jbM03uIrjST",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 17
-        },
-        "outputId": "baa5449b-af0f-4c39-f086-535c984f0509"
+        "id": "4jbM03uIrjST"
       },
       "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "            <style>\n",
-              "                .geemap-dark {\n",
-              "                    --jp-widgets-color: white;\n",
-              "                    --jp-widgets-label-color: white;\n",
-              "                    --jp-ui-font-color1: white;\n",
-              "                    --jp-layout-color2: #454545;\n",
-              "                    background-color: #383838;\n",
-              "                }\n",
-              "                    \n",
-              "                .geemap-dark .jupyter-button {\n",
-              "                    --jp-layout-color3: #383838;\n",
-              "                }\n",
-              "                \n",
-              "                .geemap-colab {\n",
-              "                    background-color: var(--colab-primary-surface-color, white);\n",
-              "                }\n",
-              "                    \n",
-              "                .geemap-colab .jupyter-button {\n",
-              "                    --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
-              "                }\n",
-              "            </style>\n",
-              "            "
-            ]
-          },
-          "metadata": {}
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -10896,9 +10763,9 @@
           "height": 17
         },
         "id": "oHtKaH0FgXTz",
-        "outputId": "35aaf721-029e-4c34-b35c-06608f1b5250"
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-      "execution_count": 6,
+      "execution_count": 4,
       "outputs": [
         {
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@@ -10963,9 +10830,9 @@
           "height": 182
         },
         "id": "Mx-DjtGNnUXk",
-        "outputId": "95a3b0ee-25cb-4547-cb3b-8da17eed9fff"
+        "outputId": "63f1438f-7513-4f05-b827-e7da084e42e0"
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+      "execution_count": 5,
       "outputs": [
         {
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@@ -11030,7 +10897,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-13420b62-a78a-44f9-8f4c-eefb76ba0e64\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-984f7484-4566-4d7a-bb13-98b36d53e7c9\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -11104,7 +10971,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13420b62-a78a-44f9-8f4c-eefb76ba0e64')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-984f7484-4566-4d7a-bb13-98b36d53e7c9')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -11156,12 +11023,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-13420b62-a78a-44f9-8f4c-eefb76ba0e64 button.colab-df-convert');\n",
+              "        document.querySelector('#df-984f7484-4566-4d7a-bb13-98b36d53e7c9 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-13420b62-a78a-44f9-8f4c-eefb76ba0e64');\n",
+              "        const element = document.querySelector('#df-984f7484-4566-4d7a-bb13-98b36d53e7c9');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -11186,7 +11053,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 8
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         }
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@@ -11223,9 +11090,9 @@
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       "outputs": [
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@@ -11270,7 +11137,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-8c1ea7e2-f14d-4848-b471-32602cc5aa9d\" class=\"colab-df-container\">\n",
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@@ -11309,7 +11176,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
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               "            title=\"Convert this dataframe to an interactive table.\"\n",
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               "\n",
@@ -11361,12 +11228,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-8c1ea7e2-f14d-4848-b471-32602cc5aa9d button.colab-df-convert');\n",
+              "        document.querySelector('#df-0f953bd2-4dce-49f0-9cdd-97927174b4a6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-8c1ea7e2-f14d-4848-b471-32602cc5aa9d');\n",
+              "        const element = document.querySelector('#df-0f953bd2-4dce-49f0-9cdd-97927174b4a6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -11391,7 +11258,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 9
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@@ -11501,9 +11368,9 @@
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@@ -11580,9 +11447,9 @@
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@@ -11673,9 +11540,9 @@
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@@ -11724,9 +11591,9 @@
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@@ -11806,9 +11673,9 @@
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@@ -11867,9 +11734,9 @@
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@@ -11939,9 +11806,9 @@
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@@ -12099,7 +11965,7 @@
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       "outputs": [
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@@ -12218,12 +12085,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
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       "outputs": [
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@@ -12389,9 +12256,9 @@
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@@ -12456,9 +12323,9 @@
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@@ -12522,9 +12389,9 @@
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@@ -12639,7 +12506,7 @@
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@@ -12713,7 +12580,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eeb9dfa5-71a0-4884-b9e5-179da6f81dea')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e76e5b36-bd13-474f-b341-abc2b55cf450')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -12765,12 +12632,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-eeb9dfa5-71a0-4884-b9e5-179da6f81dea button.colab-df-convert');\n",
+              "        document.querySelector('#df-e76e5b36-bd13-474f-b341-abc2b55cf450 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-eeb9dfa5-71a0-4884-b9e5-179da6f81dea');\n",
+              "        const element = document.querySelector('#df-e76e5b36-bd13-474f-b341-abc2b55cf450');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -12795,7 +12662,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 32
+          "execution_count": 22
         }
       ]
     },
@@ -12812,9 +12679,9 @@
           "height": 108
         },
         "id": "H3K-knJnC65R",
-        "outputId": "792b0bb6-d26c-40dd-98fa-bfbfd4d9edb9"
+        "outputId": "9a4743ad-6c95-44be-d4b3-b6656be31ea0"
       },
-      "execution_count": 33,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12903,9 +12770,9 @@
           "height": 17
         },
         "id": "i9YelZ5qDG_t",
-        "outputId": "531e7f2b-c554-4e5b-dc78-43af1810a2c9"
+        "outputId": "2652c5ad-f00a-4c16-c179-44673bd0eb79"
       },
-      "execution_count": 34,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12955,9 +12822,9 @@
           "height": 749
         },
         "id": "DP5ect8cDb7C",
-        "outputId": "176118ff-4374-4fb1-c154-bcab119d89ca"
+        "outputId": "e0ffef35-c3ee-4799-e076-24f3ae4136d5"
       },
-      "execution_count": 35,
+      "execution_count": 25,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13053,9 +12920,9 @@
           "height": 17
         },
         "id": "TJdGzd3SDisO",
-        "outputId": "09a82a63-62ba-4488-c9bd-6eaaa73d2ca8"
+        "outputId": "74452672-8591-4da0-982c-945b4dd529b9"
       },
-      "execution_count": 36,
+      "execution_count": 26,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13104,9 +12971,9 @@
           "height": 362
         },
         "id": "e3iiKaK5Eg0q",
-        "outputId": "bf150d7c-6a38-4a86-b70f-69259c8cf528"
+        "outputId": "c03e43d7-821f-44ff-cbb0-226aa989c41c"
       },
-      "execution_count": 37,
+      "execution_count": 27,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13184,9 +13051,9 @@
           "height": 441
         },
         "id": "TEFmil6WErkl",
-        "outputId": "c62cc9fd-72f1-4b5a-9793-222fe537f8fd"
+        "outputId": "c2cbd221-d66d-41da-b297-587b7009afc2"
       },
-      "execution_count": 38,
+      "execution_count": 28,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13260,9 +13127,9 @@
           "height": 52
         },
         "id": "H8uHX-83E8x7",
-        "outputId": "9a232171-e69c-4f53-e1a8-bbfb81e57e97"
+        "outputId": "d0095867-c502-487a-dd98-9a40fce553b7"
       },
-      "execution_count": 39,
+      "execution_count": 29,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13327,40 +13194,40 @@
           "base_uri": "https://localhost:8080/",
           "height": 421,
           "referenced_widgets": [
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         "id": "RswVTmLFFUb3",
-        "outputId": "c8fc2323-0b78-4960-aa67-1fc203c51186"
+        "outputId": "6c6d5f0a-9da5-4feb-e6f1-9a355e85798d"
       },
-      "execution_count": 54,
+      "execution_count": 30,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13405,7 +13272,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
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-              "model_id": "0ac7d26aa1814d8f8543b1e51934fe6e"
+              "model_id": "ad4b6510fe6441b8b18d640fefa6ed02"
             }
           },
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@@ -13441,40 +13308,40 @@
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+        "outputId": "6d375fd2-5fa9-414e-efb5-48c7ca30e506"
       },
-      "execution_count": 57,
+      "execution_count": 31,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13519,7 +13386,7 @@
             "application/vnd.jupyter.widget-view+json": {
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@@ -13551,40 +13418,40 @@
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       },
-      "execution_count": 59,
+      "execution_count": 32,
       "outputs": [
         {
           "output_type": "display_data",
@@ -13629,7 +13496,7 @@
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@@ -13661,40 +13528,40 @@
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@@ -13739,7 +13606,7 @@
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       "outputs": [
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@@ -13853,7 +13720,7 @@
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@@ -13885,40 +13752,40 @@
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@@ -13963,7 +13830,7 @@
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@@ -14041,9 +13908,9 @@
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@@ -14104,37 +13971,37 @@
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       "outputs": [
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@@ -14179,7 +14046,7 @@
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@@ -14239,9 +14106,9 @@
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       "outputs": [
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@@ -14299,38 +14166,38 @@
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       "outputs": [
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@@ -14375,7 +14242,7 @@
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@@ -14473,9 +14340,9 @@
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       "outputs": [
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@@ -14541,9 +14408,9 @@
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       "outputs": [
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@@ -14605,9 +14472,9 @@
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       "outputs": [
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@@ -14684,41 +14551,41 @@
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-            "d5a4e42464f04fd6a06619c90ade6880",
-            "7cba088702d942f6ab480723e7cef639"
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+            "e64f35fa89944b6290f597d4f7182b1c",
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+            "e10f6046737d43ad931387a38bee8442",
+            "ab81e584619a44dbb3c6be7a2453e718",
+            "0de06343152d47d686e6611d5bcedaaa",
+            "64a396f1b27048a0b54e0f35d8f7372d",
+            "d3f22884fdb545268360fa752a52fac5",
+            "0f802a5279f249dcafebb3ce0fae5a5b",
+            "2750440ca498413ea267bac448595fe3",
+            "0fdd64fd02e8442ba622ad006742441f",
+            "2d99e9a9b4f343419ad4af3fe397efeb",
+            "3cba4731c9a64a458e342a0c63a97c03",
+            "60cd78daa2204b1a9f5b6aeedd018b58",
+            "2585e9ef9fda479ba19e2102cee44d12",
+            "c6472040655249f4a40bd732d1555343",
+            "4e6ca208a3c440f0941dd02746c2cd9a",
+            "2d1da4da1fad47ffb8ae33c74a2f29cd",
+            "d1710e178fda454aa01652832eca9bd1",
+            "ab016f1ef6744faa989d2ee70f1ddcf8",
+            "29255578a88e4506a7c91dea0ab7fe1b",
+            "29d4e118ec954d48b49753449f14fca7",
+            "89827292cac042cd971c5cac3f7dba57",
+            "233f08d42af647a78a084f6e99aead56",
+            "e834223685554121abd1be7ad61abcf9",
+            "97979ca2bed04a0093e468d7c2e47691"
           ]
         },
         "id": "J_QUmonhfuSM",
-        "outputId": "76cdb481-f7df-4d9d-adf4-69374a6d37f4"
+        "outputId": "99475e65-bf76-48b7-d02c-0ea3a8a5b878"
       },
-      "execution_count": 89,
+      "execution_count": 43,
       "outputs": [
         {
           "output_type": "display_data",
@@ -14763,7 +14630,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "d8d52cc8abfc412f9babec3a2cdc14f4"
+              "model_id": "f73fe0f1f63349d3a3e3d1903cc07645"
             }
           },
           "metadata": {
@@ -14805,41 +14672,41 @@
           "base_uri": "https://localhost:8080/",
           "height": 421,
           "referenced_widgets": [
-            "c86e1a74c965421194fe6cee9b4909b6",
-            "1136a62f368c488dbd989bbe7aafe6e1",
-            "0120edb48cec4e768fb2e7a0590f2c8b",
-            "3b95748faa7a4f5c9b97d0533e14f609",
-            "e6069552718849fd8098062260ac9dfb",
-            "a4762f33f5674375a05813cbcff93403",
-            "40be0705f15f431e89c083e01eab94f9",
-            "d00f8c81b4754052850cd5ae3b67973f",
-            "08355bb00ea045fc945ee47ae4adfa7a",
-            "da37950eddb042d9bb43f495da888d89",
-            "dee5114c449f458a889ede5f5447d693",
-            "64618ee8d9fb4ec6ada73fe6a03b9a71",
-            "053f3088b4ad436da16f8269e5d07b2e",
-            "e5b7afbfdbc34e02b6d6908696a431b4",
-            "1501f246f07a4d91a19667e1090b1f73",
-            "fd25e9570f374653ae9468209fe3a54b",
-            "1b4d178364814408b78e608f67fcc078",
-            "1760d31416424e489022ad2f9d2358e7",
-            "137d487ae6d64157b96becf8e2934a84",
-            "ce79d4180d314deaac48f63581a328c9",
-            "d1bcf6455dfe466098bae22d88db7a40",
-            "d1f00923fa374f21b0d0f85337e48433",
-            "5bdea2b1052e4e3899b3cc5ccb05ebda",
-            "9f5f2770e9e145dca7d403b8e3d2deaf",
-            "1c15810c3b664092bab387df504d9211",
-            "53c2802cbe654c00bdefc54c1828299d",
-            "013047cffc06425e912d3623696c2e65",
-            "a9a30c1d7ce2494ea74f1cfbb01c355f",
-            "f3684f852fe34e2fa56e21e365ab70eb"
+            "3e90aff8906e456eb60e2c5cc946f8b1",
+            "813d63ffdac340fa82f3654ddf84b22c",
+            "e6ffba1da6054cb28715a9864b14989d",
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+            "aeaedfd96af54c82a1620ea70730bf65",
+            "068a892df4784761971df6b52b81b657",
+            "e940f18961b64bc2b95ec8b35a5c8ece",
+            "ff13f896a17041cda9d863aa9c9b7a85",
+            "36088b2ad89d403997bdba095418da88",
+            "ad7a8631b42142c2aaab712451dbbf21",
+            "33ba445201c8466092fe1bfaa03d66ce",
+            "0bfcff1d63ab4e39b5d4807d9054dd70",
+            "b27fb9823e194bdab0197c64c24d8270",
+            "b7fe85d1519349a5837c0e353cd68771",
+            "3983d97bef154958b9bed5df4760d133",
+            "84de4feaa968477f80e33d73dc99bce6",
+            "90c1dbad942044b8a5ca4ef8d81bd099",
+            "467b9855bc8c4aa28f6349957b77d984",
+            "8e55852b68324cd9981639250e21677e",
+            "9a5769371ae94e74aafbae4296637b79",
+            "a507514f7c8c4c2993cc6df6b8b2b98c",
+            "76a573d962fb426abc03edba4717ee8f",
+            "624a8bb1365341ceaf7f3dc3a02c2bcd",
+            "1a430d25cfac48d3b6e37c886c2503d6"
           ]
         },
         "id": "cBZa-J43jAL2",
-        "outputId": "fc6c0816-6828-484e-f830-9711ce3fec01"
+        "outputId": "a8c381af-8705-4337-a4cd-0926d958e43c"
       },
-      "execution_count": 90,
+      "execution_count": 44,
       "outputs": [
         {
           "output_type": "display_data",
@@ -14884,7 +14751,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "c86e1a74c965421194fe6cee9b4909b6"
+              "model_id": "3e90aff8906e456eb60e2c5cc946f8b1"
             }
           },
           "metadata": {
@@ -14963,9 +14830,9 @@
           "height": 17
         },
         "id": "uoJ0mBeRme-E",
-        "outputId": "54593455-00a4-4219-efde-127a01f86642"
+        "outputId": "57e15247-b416-4590-b046-0553d9a49885"
       },
-      "execution_count": 92,
+      "execution_count": 45,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15014,9 +14881,9 @@
           "height": 516
         },
         "id": "2nhhAUydurwm",
-        "outputId": "4594b1bd-5407-4f0d-f88b-de830ff9e3a8"
+        "outputId": "6319dc90-b167-48dc-f487-cfe57047e4bb"
       },
-      "execution_count": 93,
+      "execution_count": 46,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15056,12 +14923,12 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "slope: 8.815585915421398\n",
-            "bio14: 11.305832336890663\n",
-            "TCC: 24.970013507247337\n",
-            "bio09: 28.510260305453976\n",
-            "aspect: 3.489922562389851\n",
-            "elevation: 59.08189101408956\n"
+            "bio09: 27.06617668282514\n",
+            "bio14: 11.56570503744132\n",
+            "elevation: 51.812563029059774\n",
+            "slope: 13.908311353185116\n",
+            "aspect: 3.8891469966922387\n",
+            "TCC: 24.78555268223786\n"
           ]
         },
         {
@@ -15070,7 +14937,7 @@
             "text/plain": [
               "<Figure size 800x400 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         }
@@ -15114,10 +14981,11 @@
           "base_uri": "https://localhost:8080/",
           "height": 17
         },
+        "collapsed": true,
         "id": "sdyvrG7swBUp",
-        "outputId": "2f1dc8dd-ceee-44bf-abca-2ea272fc5208"
+        "outputId": "08adf62a-c2b4-4499-a7d0-d5e88e0f5cd3"
       },
-      "execution_count": 95,
+      "execution_count": 47,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15169,9 +15037,9 @@
           "height": 199
         },
         "id": "MccWAsdTxjUS",
-        "outputId": "b150fc57-4086-44ac-9fa4-d28cfadca49a"
+        "outputId": "9946610e-220f-4916-aab1-8a4d4abc8b8a"
       },
-      "execution_count": 97,
+      "execution_count": 48,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15211,16 +15079,16 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Iteration 1: Presence Size = 27, Pseudo-absence Size = 27\n",
-            "Iteration 2: Presence Size = 35, Pseudo-absence Size = 35\n",
-            "Iteration 3: Presence Size = 48, Pseudo-absence Size = 25\n",
-            "Iteration 4: Presence Size = 43, Pseudo-absence Size = 18\n",
-            "Iteration 5: Presence Size = 36, Pseudo-absence Size = 36\n",
-            "Iteration 6: Presence Size = 25, Pseudo-absence Size = 25\n",
-            "Iteration 7: Presence Size = 44, Pseudo-absence Size = 23\n",
-            "Iteration 8: Presence Size = 21, Pseudo-absence Size = 21\n",
-            "Iteration 9: Presence Size = 32, Pseudo-absence Size = 32\n",
-            "Iteration 10: Presence Size = 29, Pseudo-absence Size = 29\n"
+            "Iteration 1: Presence Size = 22, Pseudo-absence Size = 18\n",
+            "Iteration 2: Presence Size = 36, Pseudo-absence Size = 29\n",
+            "Iteration 3: Presence Size = 19, Pseudo-absence Size = 18\n",
+            "Iteration 4: Presence Size = 27, Pseudo-absence Size = 20\n",
+            "Iteration 5: Presence Size = 26, Pseudo-absence Size = 26\n",
+            "Iteration 6: Presence Size = 43, Pseudo-absence Size = 21\n",
+            "Iteration 7: Presence Size = 24, Pseudo-absence Size = 20\n",
+            "Iteration 8: Presence Size = 43, Pseudo-absence Size = 37\n",
+            "Iteration 9: Presence Size = 22, Pseudo-absence Size = 22\n",
+            "Iteration 10: Presence Size = 41, Pseudo-absence Size = 21\n"
           ]
         }
       ]
@@ -15289,9 +15157,9 @@
           "height": 17
         },
         "id": "GvEH7C9SzI0M",
-        "outputId": "a5e4a205-648e-4aae-b4c9-bb0cc283d04b"
+        "outputId": "c14f29fd-197d-4b08-c8f4-f6653138fc0c"
       },
-      "execution_count": 98,
+      "execution_count": 49,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15375,9 +15243,9 @@
           "height": 17
         },
         "id": "O6JACvJbzrn7",
-        "outputId": "a63108a3-6fbe-4cc4-aec1-d1ce31f99fac"
+        "outputId": "62e334aa-8fa2-49b5-a72a-3f0c6a4ef30a"
       },
-      "execution_count": 99,
+      "execution_count": 50,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15429,9 +15297,9 @@
           "height": 289
         },
         "id": "WVM4DTduz41j",
-        "outputId": "ee875256-29b8-4bf7-c579-abdad03cb901"
+        "outputId": "717561c3-77f4-4d3d-9924-4773ad536235"
       },
-      "execution_count": 100,
+      "execution_count": 51,
       "outputs": [
         {
           "output_type": "display_data",
@@ -15472,20 +15340,20 @@
           "name": "stdout",
           "text": [
             "               AUC-ROC    AUC-PR\n",
-            "Iteration 1   0.740028  0.667844\n",
-            "Iteration 2   0.794089  0.710423\n",
-            "Iteration 3   0.793023  0.796493\n",
-            "Iteration 4   0.855413  0.919148\n",
-            "Iteration 5   0.804330  0.777548\n",
-            "Iteration 6   0.752727  0.798367\n",
-            "Iteration 7   0.885093  0.762420\n",
-            "Iteration 8   0.796296  0.698985\n",
-            "Iteration 9   0.821759  0.763688\n",
-            "Iteration 10  0.892414  0.883947\n",
-            "Mean AUC-ROC = 0.8135 ± 0.0510\n",
-            "Mean AUC-PR = 0.7779 ± 0.0784\n",
-            "CPU times: user 1.9 s, sys: 199 ms, total: 2.1 s\n",
-            "Wall time: 4min 31s\n"
+            "Iteration 1   0.802778  0.758025\n",
+            "Iteration 2   0.683673  0.763472\n",
+            "Iteration 3   0.823529  0.842339\n",
+            "Iteration 4   0.706731  0.717820\n",
+            "Iteration 5   0.848077  0.764575\n",
+            "Iteration 6   0.762566  0.720878\n",
+            "Iteration 7   0.946591  0.833802\n",
+            "Iteration 8   0.891517  0.817098\n",
+            "Iteration 9   0.675000  0.622018\n",
+            "Iteration 10  0.876871  0.884682\n",
+            "Mean AUC-ROC = 0.8017 ± 0.0930\n",
+            "Mean AUC-PR = 0.7725 ± 0.0759\n",
+            "CPU times: user 1.16 s, sys: 123 ms, total: 1.29 s\n",
+            "Wall time: 3min 5s\n"
           ]
         }
       ]
@@ -15498,15 +15366,6 @@
       "metadata": {
         "id": "QDlaa47s1WjC"
       }
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "mlyD_GNF2MzK"
-      },
-      "execution_count": null,
-      "outputs": []
     }
   ]
 }
\ No newline at end of file