From 8f1d1b0ae2467dba3211687699633a92f21a2a7b Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Mon, 25 Nov 2024 20:24:51 +0100 Subject: [PATCH] Rename file --- ...iris.ipynb => iris_classification_2.ipynb} | 200 +++++++++--------- 1 file changed, 100 insertions(+), 100 deletions(-) rename iris/{ml_from_scratch_with_iris.ipynb => iris_classification_2.ipynb} (90%) diff --git a/iris/ml_from_scratch_with_iris.ipynb b/iris/iris_classification_2.ipynb similarity index 90% rename from iris/ml_from_scratch_with_iris.ipynb rename to iris/iris_classification_2.ipynb index 2f912d6..b1678b0 100644 --- a/iris/ml_from_scratch_with_iris.ipynb +++ b/iris/iris_classification_2.ipynb @@ -533,13 +533,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:35.800989Z", - "start_time": "2024-11-25T18:55:35.796515Z" + "end_time": "2024-11-25T19:22:40.599087Z", + "start_time": "2024-11-25T19:22:40.586663Z" } }, "cell_type": "code", "source": [ - "train, test = train_test_split(iris, train_size=0.3)\n", + "train, test = train_test_split(iris, test_size=0.3)\n", "print(train.shape)\n", "print(test.shape)" ], @@ -549,18 +549,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "(45, 5)\n", - "(105, 5)\n" + "(105, 5)\n", + "(45, 5)\n" ] } ], - "execution_count": 121 + "execution_count": 145 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:36.178151Z", - "start_time": "2024-11-25T18:55:36.175210Z" + "end_time": "2024-11-25T19:22:42.059105Z", + "start_time": "2024-11-25T19:22:42.054391Z" } }, "cell_type": "code", @@ -572,13 +572,13 @@ ], "id": "d4dba8b5164e8cae", "outputs": [], - "execution_count": 122 + "execution_count": 146 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:36.391735Z", - "start_time": "2024-11-25T18:55:36.385831Z" + "end_time": "2024-11-25T19:22:57.040719Z", + "start_time": "2024-11-25T19:22:57.033503Z" } }, "cell_type": "code", @@ -589,11 +589,11 @@ "data": { "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", - "132 6.4 2.8 5.6 2.2\n", - "97 6.2 2.9 4.3 1.3\n", - "143 6.8 3.2 5.9 2.3\n", - "28 5.2 3.4 1.4 0.2\n", - "42 4.4 3.2 1.3 0.2" + "111 6.4 2.7 5.3 1.9\n", + "129 7.2 3.0 5.8 1.6\n", + "113 5.7 2.5 5.0 2.0\n", + "136 6.3 3.4 5.6 2.4\n", + "19 5.1 3.8 1.5 0.3" ], "text/html": [ "
\n", @@ -622,57 +622,57 @@ " \n", " \n", " \n", - " 132\n", + " 111\n", " 6.4\n", - " 2.8\n", - " 5.6\n", - " 2.2\n", + " 2.7\n", + " 5.3\n", + " 1.9\n", " \n", " \n", - " 97\n", - " 6.2\n", - " 2.9\n", - " 4.3\n", - " 1.3\n", + " 129\n", + " 7.2\n", + " 3.0\n", + " 5.8\n", + " 1.6\n", " \n", " \n", - " 143\n", - " 6.8\n", - " 3.2\n", - " 5.9\n", - " 2.3\n", + " 113\n", + " 5.7\n", + " 2.5\n", + " 5.0\n", + " 2.0\n", " \n", " \n", - " 28\n", - " 5.2\n", + " 136\n", + " 6.3\n", " 3.4\n", - " 1.4\n", - " 0.2\n", + " 5.6\n", + " 2.4\n", " \n", " \n", - " 42\n", - " 4.4\n", - " 3.2\n", - " 1.3\n", - " 0.2\n", + " 19\n", + " 5.1\n", + " 3.8\n", + " 1.5\n", + " 0.3\n", " \n", " \n", "\n", "
" ] }, - "execution_count": 123, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 123 + "execution_count": 148 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:36.678802Z", - "start_time": "2024-11-25T18:55:36.675298Z" + "end_time": "2024-11-25T19:22:57.492247Z", + "start_time": "2024-11-25T19:22:57.489271Z" } }, "cell_type": "code", @@ -682,26 +682,26 @@ { "data": { "text/plain": [ - "132 2\n", - "97 1\n", - "143 2\n", - "28 0\n", - "42 0\n", + "111 2\n", + "129 2\n", + "113 2\n", + "136 2\n", + "19 0\n", "Name: species, dtype: int64" ] }, - "execution_count": 124, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 124 + "execution_count": 149 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:37.146459Z", - "start_time": "2024-11-25T18:55:37.141875Z" + "end_time": "2024-11-25T19:22:58.007668Z", + "start_time": "2024-11-25T19:22:58.000484Z" } }, "cell_type": "code", @@ -717,17 +717,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy of the SVM is: 0.9523809523809523\n" + "The accuracy of the SVM is: 1.0\n" ] } ], - "execution_count": 125 + "execution_count": 150 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:37.987438Z", - "start_time": "2024-11-25T18:55:37.970182Z" + "end_time": "2024-11-25T19:23:06.479606Z", + "start_time": "2024-11-25T19:23:06.461377Z" } }, "cell_type": "code", @@ -743,17 +743,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy of the Logistic Regression is 0.9523809523809523\n" + "The accuracy of the Logistic Regression is 0.9777777777777777\n" ] } ], - "execution_count": 126 + "execution_count": 151 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:55:48.868106Z", - "start_time": "2024-11-25T18:55:48.859594Z" + "end_time": "2024-11-25T19:23:07.398280Z", + "start_time": "2024-11-25T19:23:07.390791Z" } }, "cell_type": "code", @@ -769,17 +769,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy of the Decision Tree is 0.9428571428571428\n" + "The accuracy of the Decision Tree is 0.9555555555555556\n" ] } ], - "execution_count": 127 + "execution_count": 152 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:56:06.359776Z", - "start_time": "2024-11-25T18:56:06.341897Z" + "end_time": "2024-11-25T19:23:08.629708Z", + "start_time": "2024-11-25T19:23:08.619387Z" } }, "cell_type": "code", @@ -795,17 +795,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy of the KNN is 0.9714285714285714\n" + "The accuracy of the KNN is 0.9555555555555556\n" ] } ], - "execution_count": 128 + "execution_count": 153 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:58:10.222463Z", - "start_time": "2024-11-25T18:58:10.111520Z" + "end_time": "2024-11-25T19:23:18.415199Z", + "start_time": "2024-11-25T19:23:18.313591Z" } }, "cell_type": "code", @@ -826,16 +826,16 @@ { "data": { "text/plain": [ - "([,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ],\n", + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", " [Text(1, 0, '1'),\n", " Text(2, 0, '2'),\n", " Text(3, 0, '3'),\n", @@ -848,7 +848,7 @@ " Text(10, 0, '10')])" ] }, - "execution_count": 137, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" }, @@ -857,13 +857,13 @@ "text/plain": [ "
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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 137 + "execution_count": 154 }, { "metadata": {}, @@ -874,8 +874,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T18:59:03.535041Z", - "start_time": "2024-11-25T18:59:03.531779Z" + "end_time": "2024-11-25T19:23:39.668564Z", + "start_time": "2024-11-25T19:23:39.664003Z" } }, "cell_type": "code", @@ -885,13 +885,13 @@ ], "id": "9b088d596655fe48", "outputs": [], - "execution_count": 138 + "execution_count": 155 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T19:00:02.219116Z", - "start_time": "2024-11-25T19:00:02.200758Z" + "end_time": "2024-11-25T19:23:40.278456Z", + "start_time": "2024-11-25T19:23:40.270346Z" } }, "cell_type": "code", @@ -910,13 +910,13 @@ ], "id": "2e8c1859b79b3875", "outputs": [], - "execution_count": 139 + "execution_count": 156 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T19:00:23.885971Z", - "start_time": "2024-11-25T19:00:23.873029Z" + "end_time": "2024-11-25T19:23:49.826510Z", + "start_time": "2024-11-25T19:23:49.814831Z" } }, "cell_type": "code", @@ -942,13 +942,13 @@ ] } ], - "execution_count": 140 + "execution_count": 157 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T19:01:04.618866Z", - "start_time": "2024-11-25T19:01:04.602292Z" + "end_time": "2024-11-25T19:23:51.631035Z", + "start_time": "2024-11-25T19:23:51.613887Z" } }, "cell_type": "code", @@ -974,13 +974,13 @@ ] } ], - "execution_count": 142 + "execution_count": 158 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T19:01:32.092968Z", - "start_time": "2024-11-25T19:01:32.075373Z" + "end_time": "2024-11-25T19:23:53.664331Z", + "start_time": "2024-11-25T19:23:53.654733Z" } }, "cell_type": "code", @@ -1002,17 +1002,17 @@ "output_type": "stream", "text": [ "The accuracy of the Decision Tree using Petals is: 0.9555555555555556\n", - "The accuracy of the Decision Tree using Sepals is: 0.6666666666666666\n" + "The accuracy of the Decision Tree using Sepals is: 0.6444444444444445\n" ] } ], - "execution_count": 143 + "execution_count": 159 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-11-25T19:01:46.626243Z", - "start_time": "2024-11-25T19:01:46.606943Z" + "end_time": "2024-11-25T19:23:55.734286Z", + "start_time": "2024-11-25T19:23:55.720104Z" } }, "cell_type": "code", @@ -1038,7 +1038,7 @@ ] } ], - "execution_count": 144 + "execution_count": 160 }, { "metadata": {},