{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": { "id": "QIzYHOtqEMoz" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": { "id": "ap9utACrHpkf" }, "source": [ "# Upload Data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "KmbdIJwZFHuu", "outputId": "fe5a54f6-4cfa-4e8b-cb92-2e7c75cd2329" }, "outputs": [ { "data": { "text/html": [ "
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GENDERAGESMOKINGYELLOW_FINGERSANXIETYPEER_PRESSURECHRONIC DISEASEFATIGUEALLERGYWHEEZINGALCOHOL CONSUMINGCOUGHINGSHORTNESS OF BREATHSWALLOWING DIFFICULTYCHEST PAINLUNG_CANCER
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" ], "text/plain": [ " GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", "0 M 69 0 1 1 0 \n", "1 M 74 1 0 0 0 \n", "2 F 59 0 0 0 1 \n", "3 M 63 1 1 1 0 \n", "4 F 63 0 1 0 0 \n", "\n", " CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING \\\n", "0 0 1 0 1 1 1 \n", "1 1 1 1 0 0 0 \n", "2 0 1 0 1 0 1 \n", "3 0 0 0 0 1 0 \n", "4 0 0 0 1 0 1 \n", "\n", " SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER \n", "0 1 1 1 YES \n", "1 1 1 1 YES \n", "2 1 0 1 NO \n", "3 0 1 1 NO \n", "4 1 0 0 NO " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = \"DATASET/LungCancer.csv\"\n", "\n", "df = pd.read_csv(data)\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "74C4ETlJNPoJ", "outputId": "1051f82d-8bfd-4507-d954-e6e2f8de349c" }, "outputs": [ { "data": { "text/plain": [ "Index(['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY',\n", " 'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE ', 'ALLERGY ', 'WHEEZING',\n", " 'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH',\n", " 'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'LUNG_CANCER'],\n", " dtype='object')" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "X_ztsKhJMx77" }, "outputs": [], "source": [ "# mengubah column menjadi lebih gampang untuk dinamakan\n", "df.rename(columns={'FATIGUE ': 'FATIGUE'}, inplace=True)\n", "df.rename(columns={'ALLERGY ': 'ALLERGY'}, inplace=True)\n", "df.rename(columns={'LUNG_CANCER': 'DIAGNOSIS_LUNG_CANCER'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u0L6NNlRNa5f", "outputId": "889b228f-7610-4a9f-c71e-e3ac1c3f12b6" }, "outputs": [ { "data": { "text/plain": [ "Index(['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'ANXIETY',\n", " 'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE', 'ALLERGY', 'WHEEZING',\n", " 'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH',\n", " 'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'DIAGNOSIS_LUNG_CANCER'],\n", " dtype='object')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": { "id": "la2XbYQLOC8T" }, "source": [ "# EDA & Preprocessing" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "id": "_4VB_PnpN9Km", "outputId": "cb5320fa-2831-48b9-86d2-0723dd9b1cb1" }, "outputs": [ { "data": { "text/html": [ "
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AGESMOKINGYELLOW_FINGERSANXIETYPEER_PRESSURECHRONIC DISEASEFATIGUEALLERGYWHEEZINGALCOHOL CONSUMINGCOUGHINGSHORTNESS OF BREATHSWALLOWING DIFFICULTYCHEST PAIN
count309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000309.000000
mean62.6731390.5631070.5695790.4983820.5016180.5048540.6731390.5566340.5566340.5566340.5792880.6407770.4692560.556634
std8.2103010.4968060.4959380.5008080.5008080.5007870.4698270.4975880.4975880.4975880.4944740.4805510.4998630.497588
min21.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%57.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
50%62.0000001.0000001.0000000.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000000.0000001.000000
75%69.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
max87.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
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" ], "text/plain": [ " AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", "count 309.000000 309.000000 309.000000 309.000000 309.000000 \n", "mean 62.673139 0.563107 0.569579 0.498382 0.501618 \n", "std 8.210301 0.496806 0.495938 0.500808 0.500808 \n", "min 21.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 57.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 62.000000 1.000000 1.000000 0.000000 1.000000 \n", "75% 69.000000 1.000000 1.000000 1.000000 1.000000 \n", "max 87.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n", "count 309.000000 309.000000 309.000000 309.000000 309.000000 \n", "mean 0.504854 0.673139 0.556634 0.556634 0.556634 \n", "std 0.500787 0.469827 0.497588 0.497588 0.497588 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "75% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \n", "count 309.000000 309.000000 309.000000 309.000000 \n", "mean 0.579288 0.640777 0.469256 0.556634 \n", "std 0.494474 0.480551 0.499863 0.497588 \n", "min 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 0.000000 \n", "50% 1.000000 1.000000 0.000000 1.000000 \n", "75% 1.000000 1.000000 1.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 1.000000 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# untuk melihat bagaimana statistik data\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 586 }, "id": "rbdAutP9Ock9", "outputId": "71c3681a-c38c-49e6-c712-24392efa5b30" }, "outputs": [ { "data": { "text/plain": [ "GENDER 0\n", "AGE 0\n", "SMOKING 0\n", "YELLOW_FINGERS 0\n", "ANXIETY 0\n", "PEER_PRESSURE 0\n", "CHRONIC DISEASE 0\n", "FATIGUE 0\n", "ALLERGY 0\n", "WHEEZING 0\n", "ALCOHOL CONSUMING 0\n", "COUGHING 0\n", "SHORTNESS OF BREATH 0\n", "SWALLOWING DIFFICULTY 0\n", "CHEST PAIN 0\n", "DIAGNOSIS_LUNG_CANCER 0\n", "dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cek missing value apakah data sudah clean atau belum\n", "df.isna().sum()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HKyZyKpJPn-A", "outputId": "e4c5a4c7-83f0-4b18-e049-8611025a7214" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 309 entries, 0 to 308\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 GENDER 309 non-null object\n", " 1 AGE 309 non-null int64 \n", " 2 SMOKING 309 non-null int64 \n", " 3 YELLOW_FINGERS 309 non-null int64 \n", " 4 ANXIETY 309 non-null int64 \n", " 5 PEER_PRESSURE 309 non-null int64 \n", " 6 CHRONIC DISEASE 309 non-null int64 \n", " 7 FATIGUE 309 non-null int64 \n", " 8 ALLERGY 309 non-null int64 \n", " 9 WHEEZING 309 non-null int64 \n", " 10 ALCOHOL CONSUMING 309 non-null int64 \n", " 11 COUGHING 309 non-null int64 \n", " 12 SHORTNESS OF BREATH 309 non-null int64 \n", " 13 SWALLOWING DIFFICULTY 309 non-null int64 \n", " 14 CHEST PAIN 309 non-null int64 \n", " 15 DIAGNOSIS_LUNG_CANCER 309 non-null object\n", "dtypes: int64(14), object(2)\n", "memory usage: 38.8+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "TyW7l-vx_Lfl", "outputId": "51d657a4-4749-49f3-898e-e326ec8ee85c" }, "outputs": [ { "data": { "image/png": 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PVnBwsP71r3+pcuXKunjxotavX68BAwYoPj7ebizjzMlV6bLzHfnf//6nNm3a6Pjx43rkkUc0aNAgeXl56cCBA5o7d66WL1+uX3/91dr/nyeh/dON31Pp7+9DgwYNlJKSogMHDujdd9/Vpk2bdPDgQYWEhGQZG4BbT2bHX44ePapvvvlGy5Yt0xNPPKHY2FhNmDBBktS5c2ebvgkJCVq8eLE6d+5sd7WEI6mpqXrooYd06tQpbd68WY0bN7ZpnzBhgr766iulpaXlaLv+qUqVKhzjwc0zQC4bPHiwkWS2b9/uVP+IiAjTsWNHh20bN240kszSpUut8+bPn28kmd27d9v0XbNmjZFkXn75Zeu8y5cvmxIlSpgqVaqYlJQUh+vo16+fkWR27Nhhnde8eXNTvXp1M3nyZCPJ7N2719p29OhRI8lMnz490ziz+zk40qdPH1OsWLEM29u2bWssFos5dOiQXezpFi9ebCSZTZs2Zbm+jD7bjPz222/GYrGYjz/+2Ozatcvh9sbHxxtJZuLEiVkuz9FnCwC5wdl99IkTJ0yhQoVMq1atMuzTsmVL4+npaU6cOGGdJ8kMHTrUYf+lS5caSWbjxo3WeVWqVDGlSpUyFy9edHobbtzf/5OzuSp9v//xxx8bT09PM3z48EzXcfz4cVOoUCHTvn17p+ME4J4y+124fv16U6RIERMREWEuX75sjDFmwoQJRpI5ffq0Xf958+aZwoULmw0bNmT4u3T79u2mUKFCplmzZiYxMdGu/cqVK2bcuHEmNTXVOq9Pnz6mSJEipkqVKqZWrVrm+vXrNu+5cV987do1U6NGDVO0aFGzc+dOm76pqanmscceM5LMkiVLrPNfeeWVDPPFhQsXzJUrV2zi+edv94SEBGOxWMzAgQPt3nv9+nVz6tQpu/mZyWzc5Gj9NypWrJjp06eP9XV6XqhTp47dmCej5cXExBhJZtSoUXaftzHGnDx50kydOtXJLfrbjh07TKFChUyTJk0c/u13795t5s+fbze/WbNm5uGHHzajRo0y5cuXd7js7H5HUlJSTO3atU3RokXN1q1b7ZZ34cIF8+9//9tm+Zl95ukc5WBjjHnnnXeMJDNt2rQslwHg1uLM8ZcpU6aYEiVKmOTkZDNkyBBTuXLlDPtOnz7dSDJHjx512H7j/m7RokVGUrb22VnF3LFjRxMREZHpeoGc4pkayHWrV69WhQoVMr3E7UYpKSn666+/7KYLFy44vYxjx45JkkqUKGGdt23bNp07d049evSQp6fjC5XSL4FzdBn3iBEjVKJECU2cONHpONLl5HPIrn/9618yxmR6RmxERISkvy9ZTE1NdWq5Fy5csPtbnDlzxq7f4sWLVaxYMd1///265557VLFiRcXGxtr0CQoKUpEiRbR69WqdPXvWqfVfvnzZ4ffB2fgBICvO7qO/+OILpaWlObxcOl3v3r2Vmppq84yg7Pj111/166+/qnPnzpme7etIWlqaw/3luXPnsrWc8uXLq3fv3nrvvfd08uTJDPulfx6caQXc3lq1aqUXXnhBv/32mz788MMs+8fGxuq+++5Ty5YtVa1aNbvfi5I0adIkWSwWxcbG2tzWKp2Pj4+mTJlid/a7h4eHxo0bpwMHDmj58uWZxvHpp5/q4MGDeu655+zuTV6oUCH95z//UUBAgM1v/yNHjqhQoUIOr6jw8/Ozue3tjY4ePSpjjN3Zr9Lftz0KCgrKNN68Mnz4cKfGPJcvX9a0adNUvXp1TZ8+3eHVNqGhoRozZky21p/V3/7uu+9W3759beYdP35cW7duVbdu3dStWzfr2cyOZPc78v333+v5559XkyZN7Nr9/Pz00ksvOb9xWWjatKkk5drtJQEUfI6Ov6SLjY3Vww8/LC8vL3Xv3l2HDx/W7t27XbLe9Kvz8uJ3/dWrVx2OWa5du5br68atg6IGclViYqL++OMP1axZM1vv++qrr1S6dGm76cbL6f4pfcf/+++/69NPP9WkSZPk7e1tcx/cn376SZJUu3btDJeT3uboIdd+fn4aOXKkVq9erX379jm9PTn9HLIr/T6Lmf0IbtiwoZo3b6733ntPZcqUUY8ePfT222/r+PHjGb6nTZs2dn+LO+64w65fbGysHnzwQRUpUkSS9Nhjj+njjz+2KT54eHho9OjR2rt3r8qWLasOHTro5ZdfzvTznDBhgsPvQ0YPowKA7MjOPvpm84gzfvnlF0myeyC3MSbL4u4vv/zicH9Zr169bMfx/PPPKzU1NdN73WcU67Vr17IshAO4taTfX9vR7UX/6eTJk9q4caO6d+8u6e9np33yySc2BzIuX76sDRs2qEWLFipTpky2Y+nRo4cqV66syZMnyxiTYb/0AzgZFar9/f314IMP6pdfflFcXJykv08QSktL0wcffJDtuNJPLlq6dKkuX76c7ffnFT8/P40aNSrLMc+2bdt0/vx5de/e3eGtlXLi8uXLWr9+vZo1a6ayZcs6/T5nTq76J2e/I+m3Hc7u/eMdHaxLTEzM8n2OTswDcHtxdPxFkvbu3atffvlF3bp1kyQ1adJEZcqUyXRflx2//PKLAgIC7I71XLp0Kdv7sqzMnTvX4Zhl2bJlN71s3D4oaiBXpe/sHJ1hk5nIyEitW7fObpoxY0aG70nf8YeHh6tr164qVqyYVq1aZTMQunjxYpbxpLdltKNOv1pj0qRJTm9PTj+H7Eo/ozd9Ox2xWCz68ssv9eKLL6pEiRJavHixhg4dqoiICD322GMOn6kxe/Zsu7/Fjc8cOXDggH744QfrAFX6e5D6119/6csvv7TpO2nSJC1atEh169bVl19+qeeff17169dXvXr1HB4EHDRokMPvw1133ZWdjwcAHMrOPtoVecTZeG68SuPChQt2P/z3799v06dcuXIO95fOnDl9owoVKuhf//qX5syZo/j4+GzF+vnnn9vEmX4gD8Ctq0yZMvL398/yDPPFixfL29tbDz74oCSpW7duOnfunD7//HNrn7i4OKWmptoVTKW/n2mR1VmdhQoV0rhx4/T9999rxYoVGcby008/yd/fP9N91I2F6v79+6t06dLq27evqlWrpiFDhmjx4sVOXVEeGhqq3r1767PPPlOZMmX08MMPa+bMmdYCcUHy1FNPZTnmyaiw7eiqwcwKB/8UFxenlJSUbJ8M5szJVf/k7Hfk559/lr+/v8LDw52O5dKlSw4P1v3zOY/pLl68qL/++kvx8fH68ssvNXLkSFksFqee9QHg1uTo+Iv0934uODhYLVu2lPT3sZ3HHntMS5YscclzLhITEx1eJf7888/b7Mt69Ohx0+t68MEHHY5Z0rcNcAYPCkeu8vPzk5T5QXZHSpUqpTZt2tjNz+iWUdLfO/4qVarowoULmjdvnrZs2SJvb2+bPukHmjKLJ6sDVv7+/ho5cqQmTJig7777zqmzaHL6OWRXUlKSpKwPzHl7e+v555/X888/r/j4eG3evFmvv/66Pv74YxUuXNju4Nc999yT5YPCP/zwQxUrVkwVKlSwnsnm4+OjcuXKKTY2Vh07drTp3717d3Xv3l2JiYnatWuXFixYoEWLFqlTp046ePCgzaX7lStXdvh9AABXyM4+2hV5JCPpt+1If1/6Pj1d8eLFrYOar776yubBtemKFSvmcH+ZfuZndo0bN04ffPCBpk6dqtdff92uPaNYGzdubI11+vTp2r59e47WD8C9FC9ePMt9afrvwvT9R+XKlVW/fn3FxsZar8rOqGAq/V1w/WcBYenSperatatdv549e+rFF1/U5MmT1blzZ4e3Rrp48WKW++obC9XBwcH6/vvvNXnyZC1fvlzvvvuu3n33XXl5eWncuHEaN26cw3Wlmz9/vu655x7NmzdPy5cv1/Lly/XMM8+oVatWWrhwocOrofPDjWOeunXr2vXJ6O/0ww8/2PU/ffq0SpUqleV6c3IyWPrJVTExMdZ53bt318svv6wvv/zSbhySzpnvSGJiYrbzuY+Pj8OHrDva/v79+9u8Ll26tD744AM1aNAgW+sEcOtwdPwlLS1NS5YsUcuWLXX06FHr/MjISM2cOVPr169X27Ztb2q9vr6+Dq+ufvLJJ613QMnJrakc7VvLlCnDMR7cNK7UQK7y8/NTWFiYDh48mOvruueee9SmTRt16dJFq1atUo0aNdSjRw+bAy3VqlWT9PcP34ykt2V2FcCIESMUEBDg9NUaefU5pC+/UqVKTr8nNDRU3bp105YtW1S5cuVMz2jKiDFGixcv1qVLl3TXXXepcuXK1unYsWNauXKl3QGvdH5+frrvvvsUGxurPn366MiRI9q1a1e21g8ANyM7++ic5hFvb29duXLFYf/0W5CkF3OrVq0qSXbxeHp6qk2bNmrTpk2eXalWoUIF9erVK8OrNTKKtXTp0tZYQ0ND8yRWAPkvKSkp0wPAP//8s7777js1btxYcXFx1qlFixZas2aN3QFtR78fV65cmeUV3NL/nYm/f//+DM/E9/X1zbII46hQHRoaqnfeeUfx8fE6dOiQ3njjDZUuXVrjx4/X3LlzM12eh4eHhg4dqr179+qvv/7SypUrFRUVpQ0bNlhvKZKXMivApI95Mnq2RkZ/p0qVKlnPus3ubZtycjLYjSdXxcXF2ZxclRFnviN+fn7ZPjGtUKFC1hz4z6lOnTp2fcePH69169Zp+fLl6t27ty5cuCAPDw7TALC1YcMGxcfHa8mSJTbHW9KvAHPFLaiqVq2q8+fP648//rCZX6VKFet+7MbnRqW/zmyck9mzpoCbQbZErrv//vt15MgR7dixI8/WWahQIcXExOjkyZN66623rPObNGmigIAALVq0KMPL8xYuXChJNs/iuFH6mUsrV67Ud99951RMefE5fPDBB7JYLLrvvvuy/d7ChQurVq1a1oe0Z8fmzZv1+++/a/LkyVq6dKnNNGfOHF2+fDnTy7rTpZ+NkNFtTgAgtzi7j46KilKhQoUyvZf6woU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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cek imbalance/balance data yang ada pada dataset\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "binary_cols = df.columns.difference(['AGE', 'GENDER'])\n", "\n", "n_cols = 4\n", "n_rows = (len(binary_cols) + n_cols - 1) // n_cols\n", "fig, axes = plt.subplots(n_rows, n_cols, figsize=(16, n_rows * 3))\n", "axes = axes.flatten()\n", "\n", "\n", "for i, col in enumerate(binary_cols):\n", " sns.countplot(data=df, x=col, hue=col, ax=axes[i], palette='Blues', legend=False)\n", " axes[i].set_title(col)\n", " axes[i].set_xlabel('')\n", " axes[i].set_ylabel('count')\n", "\n", "\n", "for j in range(i+1, len(axes)):\n", " fig.delaxes(axes[j])\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "CWONwG-pt2CU" }, "source": [ "Terlihat jelas bahwa target dari Diagnosis Lung Cancer memiliki data imbalance yang sangat jauh. Maka dari itu dibutuhkan SMOTE untuk menghindari terjadinya overfitting. Untuk feature-feature lain, bisa dibilang masih relatif aman dan tidak terlalu jauh perbedaannya" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "mdkCAku_EKiS", "outputId": "2b74b9f1-ffa1-47a4-d4f4-b4dede59c4bf" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2317/2262685366.py:2: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.boxplot(x=df['AGE'], palette='Blues')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 4))\n", "sns.boxplot(x=df['AGE'], palette='Blues')\n", "plt.title(\"Boxplot of AGE\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "la4V-aW6t5O9" }, "source": [ "Rata rata AGE dominan di kanan pada sekitar umur 55 - 70 dari box plot tersebut" ] }, { "cell_type": "markdown", "metadata": { "id": "2BvQHS--ShdY" }, "source": [ "# Plot" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "s-3NEDS8OnO4", "outputId": "27ef9398-b345-4cff-f47d-2537e6e791f4" }, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " , , ]],\n", " dtype=object)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AAedjb15nT4q+4xs4cKA5deqUy7rNmzebiIgI07p1a+NwOJzLPf2+X1rFvdf33nuvkWS++eYb57KizzC//PKLy9iBAwcaSc7Pq+fadnHWrl1rJJm1a9ea0NBQs2DBgmLHjhkzxhT3Fb6n9/pMxeXA77iUFgKqS5cu+vLLL106nBs3blTLli3Vp08fbdq0SYWFhS7rbDabOnfuHIjpltqrr76q9PR03Xnnnerfv3+pnvPggw/q9OnTeuKJJ0oc9+OPP+qVV15R7969dffdd3sc06RJE911111ezxsAipOenq5atWrpmmuu0U033aT09HSftvPbb78pLS1NzZs318yZM2Wz2dzGDBs2TB07dpQk/eMf/1BOTo6eeuopt7+Oqlatml577TXZbDZNmzbNp/kUzempp55S06ZNlZaW5ra+X79+Gj58uFatWuVyyUVf/eMf/9BPP/2kZ555xvnXVmeKi4vTww8/fN5zq1mzpsaPH6/ly5friy++KHFOe/bs0UUXXaT69eu7rYuNjfUmXrGGDBmirVu3upx+n5OTo7Vr12rIkCFebWvcuHGqVauWpkyZUuK448eP68knn1TLli311FNPefz3lpCQoPvvv9+r/QPA2WbMmKFjx45p3rx5SkhIcFt/8cUX6+6779bevXs1b948XX311R4vjTVmzBh169ZNr7zyivbu3evzfCZNmqTatWtr3rx5CgsLc1sfFRV1zmPo+eCzDYBA2rNnj1q2bKno6Gi3def7u+1TTz2l48eP66WXXlKdOnVc1tWuXVv/+Mc/lJ+frxkzZpxzW0Vngvh69p43v++XVtFcPH3vFhwcrAsuuMBl7Pm8zlOnTlWtWrX00ksvKTg42GVdx44ddf/992vbtm3FXiK5rMTHx0uSQkLOfWGl833PiqSnp+uSSy5Rt27dlJKS4vPna5wfGiMIqC5dusjhcGjz5s3OZUWXT+nUqZOOHDmi7du3u6xr3ry5y4HY4XDowIEDbj9Hjhwpdr9HjhxxG+/p2oUnTpzwuO2TJ08Wu+2dO3dq3LhxatOmjZ555plSvxYNGzbULbfcopdffln79u0rdtz777+v06dP6y9/+Uuptw0A5ys9PV39+/dXWFiYBg8erG+//VZbtmzxejsbNmzQoUOHNGTIELdffj1Zvny5wsPDdfPNN3tc37BhQ3Xp0kVr1651O4346NGjHo/hZ1+nfMOGDfr11181ZMiQYn8ZLrqs4ooVK0oTs0TvvvuuqlWrpptuuumcY893bnfffXepGgj169fXjz/+6PH+LmXliiuu0EUXXaSFCxc6l7355puqUaOGrrnmGq+2FRkZWaoPgRs2bNDhw4c1ePDgUv17AwBfLV++XI0aNXJeBrI4Rb/Le7pcb5FbbrlFp06dOue10ovzzTff6JtvvtENN9xQ7H2XinP69GmPtfPXX3/1ajt8tgEQSPXr11dWVpbL90kl8eZ7peXLl6tBgwYul7c60xVXXKEGDRrovffeO+d+v//+e0lSrVq1SjVPT0r7+36R4j4jGWMkyfmHUunp6Tp16lSJ2/L2dT7Tt99+q6+//lrXX3+9IiMjPY4p6XOON98BnunM93rv3r1avny5nnnmGV1xxRUeL7l/tpLes+L+HZ39ObWgoED//ve/NXjwYEnS4MGDtXbtWuXk5JQqgyfHjx/3uO/jx4/7vE0roDGCgDrzPiOSdOrUKW3evFmdO3dW48aNFRcX51x39OhRbdu2zfmcIqtXr1adOnXcfm644YZi95uSkuI2/sILL3QbN2/ePI/bLu566L/99pvzy7s333xT4eHhXr0eDz30kE6dOqUnn3yy2DFFf2l79k0LT548ec5GDwD4IisrS7t27XLe36lLly666KKLfPqrlp07d0qSWrduXarxX331lZo1aya73V7smEsvvVQOh0O7d+92WX7bbbd5PIafeWPBon0UbaekfZw5//Oxc+dONW3a1ONf8J7tfOcWGRmpe+6555wNhL/97W8KCwtT9+7d1a5dO91zzz165513yvQXaZvNpkGDBumNN95wLitquJX0/pY051q1apV4/5PiaqanL/6KPgwCgLfy8vL0008/laq2+aPmFHfsM8a4HfvO/tJr165dHmvnn/70J6/nwWcbAIFy77336vjx42rbtq06deqk+++/X6tXr5bD4fA4vrTfKx05ckT79u07570s2rRpo7179+ro0aNuzy/6Qv7f//63pk6dKrvdXuzN30ujtL/vFynuM1Jubq4k6fLLL9eVV16pl19+WRdddJGGDBmiF154QT/88IPbtrx9nc9UmnrYoEEDRUZGutXD/Px8t/lff/3159yn5Ppe161bV9ddd50aNmxY7Pd8hw4d0oEDB/S///1P8+fP15w5c1SnTh1dccUVJW77zJ/nnnvOZdyKFSt0+PBh5+frG264QaGhoW6fU70xefJkj/t+6qmnfN6mFXDzdQRUixYtdMEFFzibH//5z3+Un5/v/EurTp06aePGjbrrrruUmZmp06dPuzVGkpKS9Oijj7pt+z//+Y/uvfdej/udM2eO2w3RPf0l6fXXX+/xFPfiPvTcfffd2r59u+bNm+fx8ijn0qhRIw0bNkwvvfSSHnjgAY+n4efl5UmS219/rVy50uXmXtWrV9exY8e8ngMAnC09PV1xcXHq1q2bpN+/4B44cKD+9a9/6emnn/bqL/GLjmFFN5Y7lzNvQlecovVF2y4yadIkj3/J9dRTT2njxo0u+zjXnIrbhy/y8vK8yn++c7v77rs1a9YsTZ06Ve+8847HMS1bttTWrVs1ffp0rVixQlu3btVzzz2nGjVq6JlnntGoUaNKNd9zGTJkiGbOnKktW7aoVq1a2rJlix5//HGfthUVFaV77rlHkydP1pdffql27dq5jSmuZm7bts1t/C+//KLatWv7NBcA1uZNbfNHzSnu2HfkyBG3y75s2bJFHTp0cD5u0KCBXn75Zbdt5ubmen1WB59tAARKjx49lJmZqbS0NH3wwQfKzMzUjBkzVKdOHb3yyiu67rrrXMaX9nul0hzDz1x/9u/9Z95cXfr9mPuvf/1LF110kXcBz1Ka3/eLFPcZKSYmRtLvn/U++OADzZw5U//617/0xhtv6I033tCYMWN088036x//+Ifz0lnevs5n8ua1PLsehoeHa/ny5S7LSnvWzZnvdUFBgf7zn//oqaee0nXXXacPP/xQ1apVcxl/9o3fW7durfnz5ysiIqLEbZ+pSZMmLo/T09PVoUMHXXzxxc6M11xzjdLT03XPPfeUKsfZRo8erT//+c9uy19//XW3m7bjDzRGEFA2m02dOnXS+vXrVVhYqI0bNyo2NtZ5cOjUqZP+7//+T5KcX2Kd3RipXbu2W3GRSr42YMeOHV0+ABTnoosu8rhtT9588029/PLLGjx4sG677bZSPceThx9+WP/85z/1xBNPuHWVpT+KxtkfDDp37qyMjAxJ7l/6AYCvTp8+rUWLFqlbt27Kzs52Lk9KStLTTz+tNWvWqGfPnqXeXtFp0mf/9VRxatasec6xxf1S3bp1a4/H8H/9619u+zjXnEr7i3tpREZGepX/fOd2dgOhuA8NTZs21T//+U+dPn1aX331lVasWKEZM2Zo9OjRatiwYanroSSP9/KQpHbt2ql58+ZauHChoqOjFR8fr6uvvrrU2z3b3XffrWeffVZTpkzx+CGwuJp58cUXO2smHxYAnC9valt51pyiY29xx74aNWo4j32rV6/2+Fek1atX93i8L7p0iLf4bAMgUC677DItWbJEJ0+e1H/+8x8tXbpUzz77rG666SZt3bpVl1xyiXNsab9XKs0x/Mz1Zx/Hi/5I98iRI3r11Ve1fv16n86cPltpf9+Xiv+MdCa73a6HHnpIDz30kPbv36+PP/5Yzz33nN566y2Fhoa6fJ7y5nU+kzev5dn3KwkODvbqs8mZzn6vr7nmGjVr1kw33XSTXnnlFY0bN85l/L///W9FRkbql19+0fPPP6/s7Gy35klx2/bk8OHDWrlypcaOHetyxYPOnTvr3//+t7755hu3P+QujSZNmnjcd9EfosMzLqWFgOvSpYuOHDmibdu2Oe8vUqRTp0763//+p59++kkbNmxQYmKiGjVqFMDZerZnzx6NHj1ajRs31j/+8Y/z2lajRo30l7/8RS+99JL279/vtr7oTJSzr+FYp04dpaSkKCUlxeNfYwGAL9auXav9+/dr0aJFatKkifOn6LKB3l5Oq+gYtm3btlKNb9Gihb7++mu3+4Kc6b///a9CQ0Pd/hKntFq0aOHcTkn7kFTsL/beaN68ub755psS71dV1nO7++67FR0dXeJlp4oEBwerdevWmjhxopYuXSrJ9X0ODw93u05ukaJLb5V0KckhQ4bozTff1MKFCzVw4EAFBfn+62jRh8B3331XX375pdv64mpmjRo1nDWzIv5eAaByiYyMVGJiYqmuse7rcd1ut5f62FvcsS8kJMR57CuLelYafLYBEGhhYWG67LLL9Pjjj+vFF1+Uw+HQ4sWLfdpWVFSUEhISSjyGS78fxy+88EK3e2d07NhRKSkpGjBggN599121atVKQ4YMKZMz4rz5fd8bCQkJGjRokNavX68mTZrorbfe8njvEW9f59LUw//973/Ky8sr95rVvXt3SdL69evd1l1xxRVKSUnR4MGDlZGRoWrVqmno0KEqLCz0aV+LFy9WQUGBnn76aZfP16mpqZK8/3yN80NjBAF35n1GNm7cqM6dOzvXtW/fXna7XevWrXPee6SiOXnypAYOHKgTJ05o0aJFZfLXxA8//HCx1+Pt06ePgoODOVgC8Iv09HTFxsZq8eLFbj+DBw/W0qVLi/2ixpMuXbqoVq1aeuONN3T69Olzjr/22mt14sSJYn+p/v777/XJJ5/o6quvLvYvd0ozp+joaC1cuLDYOb3++uvO+Zyvfv366bffftO///1vv82tqIHwzjvveGwgFKfo7Mozv8y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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot numerical columns\n", "num_columns = [column for column in df.columns if type(df[column]) != 'object']\n", "df.hist(column = num_columns, figsize = [20, 12])" ] }, { "cell_type": "markdown", "metadata": { "id": "Gw6fNTwpPCbn" }, "source": [ "Dari plotting diatas, bisa dilihat bahwa pada Age data terdistribusi secara Left Skewed dimana data condong ke kanan dan ekornya berada di kiri. Hal ini bisa menunjukkan bahwa pasien yang terdiagnosis kanker paru paru biasanya berada di rentan umur yang lebih tua dibandingkan umur yang lebih muda. Untuk faktor faktor lainnya, ada beberapa yang mempengaruhi seperti SMOKING', 'YELLOW_FINGERS', 'ANXIETY', 'PEER_PRESSURE', 'CHRONIC DISEASE', 'FATIGUE', 'ALLERGY', 'WHEEZING', 'ALCOHOL CONSUMING', 'COUGHING', 'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN. Terlihat pada visualisasi diatas bahwa beberapa mempengaruhi adanya kanker paru paru terutama pada 'SMOKING', 'YELLOW_FINGERS', 'FATIGUE', 'COUGHING', 'SHORTNESS OF BREATH', dan lain-lain." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "Zn9aFDYNQcbU", "outputId": "33229f4d-2bfa-43bd-86a6-415b1f969613" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot categorical columns\n", "categoric_columns = [column for column in df.columns if df[column].dtype == 'object']\n", "import math\n", "\n", "n_cols = 3\n", "n_rows = math.ceil(len(categoric_columns) / n_cols)\n", "\n", "plt.figure(figsize=(8 * n_cols, 6 * n_rows))\n", "\n", "for i, column in enumerate(categoric_columns):\n", " plt.subplot(n_rows, n_cols, i + 1)\n", " sns.countplot(data=df, x=column)\n", " plt.title(f'{column}')\n", " plt.xticks(rotation=45)\n", " plt.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "WOUwwF_hRrIx" }, "source": [ "Dari Visualisasi plot categorical column diatas menunjukkan bahwa data lebih banyak laki laki dibandingkan perempuan. Dan untuk perbandingan terkena kanker atau tidaknya lebih banyak yang terkena kanker dibandingkan dengan yang tidak/ sehat." ] }, { "cell_type": "markdown", "metadata": { "id": "nisqLWXiSlYY" }, "source": [ "# Encoding Categorical Columns" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_8XuzbEiRpGN", "outputId": "4e8438ed-a052-4a86-a558-7eb810a8371c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Mapping for column 'GENDER':\n", " F -> 0\n", " M -> 1\n", "\n", "Mapping for column 'DIAGNOSIS_LUNG_CANCER':\n", " NO -> 0\n", " YES -> 1\n" ] } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "\n", "# Encode setiap categorical column menggunakan label encoder\n", "label_encoders = {}\n", "\n", "# Encode dan simpan encoder-nya\n", "for col in categoric_columns:\n", " le = LabelEncoder()\n", " df[col] = le.fit_transform(df[col])\n", " label_encoders[col] = le # simpan encoder-nya\n", "\n", "for col, le in label_encoders.items():\n", " print(f\"\\nMapping for column '{col}':\")\n", " for i, class_ in enumerate(le.classes_):\n", " print(f\" {class_} -> {i}\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 855 }, "id": "AAs52tnfTZ5d", "outputId": "afdb6a16-1945-43c9-84df-f8994f1d8158" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_matrix = df.corr()\n", "plt.figure(figsize=[12,8])\n", "sns.heatmap(corr_matrix, annot = True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 586 }, "id": "4ZyKLibeUkYX", "outputId": "a9073eee-fd7f-4c26-a0d8-7934c765bfef" }, "outputs": [ { "data": { "text/plain": [ "GENDER 0.067254\n", "AGE 0.089465\n", "SMOKING 0.058179\n", "YELLOW_FINGERS 0.181339\n", "ANXIETY 0.144947\n", "PEER_PRESSURE 0.186388\n", "CHRONIC DISEASE 0.110891\n", "FATIGUE 0.150673\n", "ALLERGY 0.327766\n", "WHEEZING 0.249300\n", "ALCOHOL CONSUMING 0.288533\n", "COUGHING 0.248570\n", "SHORTNESS OF BREATH 0.060738\n", "SWALLOWING DIFFICULTY 0.259730\n", "CHEST PAIN 0.190451\n", "DIAGNOSIS_LUNG_CANCER 1.000000\n", "Name: DIAGNOSIS_LUNG_CANCER, dtype: float64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr_matrix['DIAGNOSIS_LUNG_CANCER']" ] }, { "cell_type": "markdown", "metadata": { "id": "zhdTz1SQUG9v" }, "source": [ "Berdasarkan analisis map correlation, terdapat beberapa fitur yang menunjukkan hubungan yang cukup kuat terhadap variabel target DIAGNOSIS_LUNG_CANCER. Fitur dengan korelasi tertinggi adalah ALCOHOL CONSUMING dengan nilai korelasi sebesar 0.33, diikuti oleh CHEST PAIN dengan korelasi 0.31. Selanjutnya, FATIGUE dan SWALLOWING DIFFICULTY memiliki korelasi masing-masing sebesar 0.26, sedangkan COUGHING dan WHEEZING masing-masing menunjukkan korelasi sebesar 0.25 terhadap diagnosis kanker paru-paru. Nilai-nilai korelasi ini menunjukkan bahwa terdapat hubungan positif antara kemunculan gejala atau kondisi tersebut dengan kemungkinan seseorang didiagnosis menderita kanker paru-paru. Dengan demikian, fitur-fitur tersebut dapat dianggap sebagai faktor penting dan relevan dalam membangun model prediksi untuk mendeteksi kanker paru-paru secara dini." ] }, { "cell_type": "markdown", "metadata": { "id": "Y_D67R0UUwvf" }, "source": [ "Modeling & Training" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "zmwyPGszUwVD" }, "outputs": [], "source": [ "X = df.drop(['DIAGNOSIS_LUNG_CANCER'], axis = 1)\n", "y = df['DIAGNOSIS_LUNG_CANCER']" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "id": "5f6fREE0VGVi" }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# Dataset dibagi menjadi train, validation, dan test:\n", "# Train set digunakan untuk melatih model.\n", "# Validation set digunakan untuk menyetel atau menyesuaikan parameter model (tuning).\n", "# Test set hanya digunakan sekali di akhir, untuk mengevaluasi performa akhir model terhadap data yang benar-benar belum pernah dilihat sebelumnya (unseen data).\n", "# Gunakan Stratify untuk menghindari terjadinya data yang bias\n", "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.2, random_state=42, stratify=y_temp)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "id": "nU0yurHIVzBY" }, "outputs": [], "source": [ "from imblearn.pipeline import Pipeline\n", "from imblearn.over_sampling import SMOTE\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import StratifiedKFold, cross_val_score\n", "\n", "def cross_validate(model, X, y):\n", " kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", " # Melakukan smoting dan scaling pada setiap model\n", " pipeline = Pipeline(steps=[\n", " ('scaler', StandardScaler()),\n", " ('smote', SMOTE(random_state=42)),\n", " ('model', model)\n", " ])\n", " scores = cross_val_score(pipeline, X, y, scoring='f1_macro', cv=kfold, n_jobs=-1)\n", " return scores" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nHIIkmq5V17g", "outputId": "4b2e45d7-ee19-4130-96df-9b534a9914e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR: 0.7514650178603097 +- (0.07377098379472546)\n", "KNN: 0.7672938097290665 +- (0.0639263271898173)\n", "SVC: 0.742281898266553 +- (0.09175015868306588)\n", "DT: 0.7502308711305978 +- (0.13967203147563148)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RF: 0.7760325152314432 +- (0.07851834003159625)\n", "XGB: 0.7860213669594254 +- (0.08529173000531132)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vscode/.local/lib/python3.11/site-packages/xgboost/training.py:183: UserWarning: [03:55:22] WARNING: /workspace/src/learner.cc:738: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " bst.update(dtrain, iteration=i, fobj=obj)\n", "/home/vscode/.local/lib/python3.11/site-packages/xgboost/training.py:183: UserWarning: [03:55:22] WARNING: /workspace/src/learner.cc:738: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " bst.update(dtrain, iteration=i, fobj=obj)\n", "/home/vscode/.local/lib/python3.11/site-packages/xgboost/training.py:183: UserWarning: [03:55:22] WARNING: /workspace/src/learner.cc:738: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " bst.update(dtrain, iteration=i, fobj=obj)\n", "/home/vscode/.local/lib/python3.11/site-packages/xgboost/training.py:183: UserWarning: [03:55:22] WARNING: /workspace/src/learner.cc:738: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " bst.update(dtrain, iteration=i, fobj=obj)\n", "/home/vscode/.local/lib/python3.11/site-packages/xgboost/training.py:183: UserWarning: [03:55:22] WARNING: /workspace/src/learner.cc:738: \n", "Parameters: { \"use_label_encoder\" } are not used.\n", "\n", " bst.update(dtrain, iteration=i, fobj=obj)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "MLP: 0.72250735801567 +- (0.07413742611335593)\n", "NB: 0.7534099913433163 +- (0.10625690606068657)\n", "[LightGBM] [Info] Number of positive: 137, number of negative: 137\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000028 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 230\n", "[LightGBM] [Info] Number of data points in the train set: 274, number of used features: 15\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Info] Number of positive: 137, number of negative: 137\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000025 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Info] Total Bins 212\n", "[LightGBM] [Info] Number of data points in the train set: 274, number of used features: 15[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Info] Number of positive: 138, number of negative: 138\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000027 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 217\n", "[LightGBM] [Info] Number of data points in the train set: 276, number of used features: 15\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Info] Number of positive: 138, number of negative: 138\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000023 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Info] Total Bins 249\n", "[LightGBM] [Info] Number of data points in the train set: 276, number of used features: 15\n", "\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Info] Number of positive: 138, number of negative: 138\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000026 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 236\n", "[LightGBM] [Info] Number of data points in the train set: 276, number of used features: 15\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "LGBM: 0.7639302091447113 +- (0.08342510660355626)\n", "LDA: 0.7857639909636666 +- (0.07297709147416942)\n", "QDA: 0.6004186139814323 +- (0.11935145592635654)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/vscode/.local/lib/python3.11/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear\n", " warnings.warn(\"Variables are collinear\")\n", "/home/vscode/.local/lib/python3.11/site-packages/sklearn/discriminant_analysis.py:935: UserWarning: Variables are collinear\n", " warnings.warn(\"Variables are collinear\")\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neural_network import MLPClassifier\n", "from xgboost import XGBClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from lightgbm import LGBMClassifier\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n", "\n", "# mencoba beberapa klasifikasi model untuk mencari model yang terbaik\n", "models = []\n", "models.append(('LR', LogisticRegression(max_iter=1000)))\n", "models.append(('KNN', KNeighborsClassifier()))\n", "models.append(('SVC', SVC()))\n", "models.append(('DT', DecisionTreeClassifier()))\n", "models.append(('RF', RandomForestClassifier()))\n", "models.append(('XGB', XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')))\n", "models.append(('MLP', MLPClassifier(max_iter=1000)))\n", "models.append(('NB', GaussianNB()))\n", "models.append(('LGBM', LGBMClassifier()))\n", "models.append(('LDA', LinearDiscriminantAnalysis()))\n", "models.append(('QDA', QuadraticDiscriminantAnalysis()))\n", "\n", "results = []\n", "names = []\n", "\n", "# print setiap model untuk mencari model yang terbaik\n", "for name, model in models:\n", " scores = cross_validate(model, X_train, y_train)\n", " results.append(scores)\n", " names.append(name)\n", " print(f'{name}: {scores.mean()} +- ({scores.std()})')" ] }, { "cell_type": "markdown", "metadata": { "id": "BfVSXBTTB9-X" }, "source": [ "Dari sini dapat dilihat bahwa XGBoost memiliki skor tertinggi" ] }, { "cell_type": "markdown", "metadata": { "id": "RZY1QskpCFMj" }, "source": [ "# XGBoost Evaluation Model" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "kXiOH7PcCJ5-", "outputId": "7057ed31-5805-4ea0-aa88-aec91773e5b7" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n", "\n", "from itertools import cycle\n", "\n", "model = XGBClassifier()\n", "model.fit(X_train, y_train)\n", "\n", "y_true = y_val\n", "y_pred = model.predict(X_val)\n", "y_pred_proba = model.predict_proba(X_val)\n", "\n", "class_labels = ['0','1']\n", "n_classes = len(class_labels)\n", "cm = confusion_matrix(y_true, y_pred)\n", "plt.figure(figsize=(8, 6))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n", "plt.title('Confusion Matrix')\n", "plt.ylabel('Actual Class')\n", "plt.xlabel('Predicted Class')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "OTj-o8cWE3jS" }, "source": [ "Kategori\tPenjelasan\tNilai\n", "True Negative (TN)\tModel memprediksi tidak terkena kanker (0), dan benar\t5\n", "False Positive (FP)\tModel memprediksi terkena kanker (1), padahal seharusnya tidak\t1\n", "False Negative (FN)\tModel memprediksi tidak terkena kanker (0), padahal seharusnya terkena\t2\n", "True Positive (TP)\tModel memprediksi terkena kanker (1), dan benar\t42\n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 859 }, "id": "03xTtiivE6B8", "outputId": "4e899804-d813-4c8b-9afd-cd779a6e1883" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 0.71 0.83 0.77 6\n", " 1 0.98 0.95 0.97 44\n", "\n", " accuracy 0.94 50\n", " macro avg 0.85 0.89 0.87 50\n", "weighted avg 0.95 0.94 0.94 50\n", "\n", "--Evaluation Metrics--\n", "Accuracy : 0.94\n", "Precision: 0.9767441860465116\n", "Recall : 0.9545454545454546\n", "F1 Score : 0.9655172413793104\n", "--ROC AUC Score--\n", "ROC AUC Score: 0.9696969696969697\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Evaluation Metrics\n", "print(\"Classification Report:\\n\", classification_report(y_true, y_pred, target_names=class_labels))\n", "print('--Evaluation Metrics--')\n", "print(\"Accuracy :\", accuracy_score(y_true, y_pred))\n", "print(\"Precision:\", precision_score(y_true, y_pred))\n", "print(\"Recall :\", recall_score(y_true, y_pred))\n", "print(\"F1 Score :\", f1_score(y_true, y_pred))\n", "\n", "# ROC AUC Score and Curve\n", "print('--ROC AUC Score--')\n", "roc_auc = roc_auc_score(y_true, y_pred_proba[:, 1])\n", "print(\"ROC AUC Score:\", roc_auc)\n", "\n", "# Hitung FPR, TPR\n", "fpr, tpr, thresholds = roc_curve(y_true, y_pred_proba[:, 1])\n", "\n", "# Plot ROC Curve\n", "plt.figure(figsize=(8, 6))\n", "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') # garis random\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate (1 - Specificity)')\n", "plt.ylabel('True Positive Rate (Sensitivity / Recall)')\n", "plt.title('ROC Curve - Binary Classification')\n", "plt.legend(loc=\"lower right\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "C_4Fdk2NHKFb" }, "source": [ "# Tuning Model" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "_HfTHciFHRt5" }, "outputs": [], "source": [ "X = df.drop(['DIAGNOSIS_LUNG_CANCER'], axis = 1)\n", "y = df['DIAGNOSIS_LUNG_CANCER']\n", "\n", "X_temp, X_test, y_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", "X_train, X_val, y_train, y_val = train_test_split(X_temp, y_temp, test_size=0.2, random_state=42, stratify=y_temp)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e-H4KsSxHj8x", "outputId": "003b3df8-99a8-4ea3-c349-d72acc0365e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n", "Best Parameters: {'model__subsample': 0.8, 'model__reg_lambda': 10, 'model__reg_alpha': 0.1, 'model__n_estimators': 400, 'model__max_depth': 3, 'model__gamma': 0.2, 'model__eta': 0.2, 'model__colsample_bytree': 1.0}\n", "Best Cross-Validation f1 macro: 0.8356389986824769\n" ] } ], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "\n", "model = XGBClassifier()\n", "\n", "pipeline = Pipeline([\n", " ('scaler', StandardScaler()),\n", " ('smote', SMOTE(random_state=42)),\n", " ('model', model)\n", "])\n", "\n", "# hyperparameter tuning\n", "param_dist = {\n", " 'model__n_estimators': [100, 200, 300, 400, 500],\n", " 'model__max_depth': [3, 5, 7, 10],\n", " 'model__eta': [0.01, 0.05, 0.1, 0.2],\n", " 'model__subsample': [0.6, 0.8, 1.0],\n", " 'model__colsample_bytree': [0.6, 0.8, 1.0],\n", " 'model__gamma': [0, 0.1, 0.2, 0.5, 1.0],\n", " 'model__reg_lambda': [0, 0.1, 1, 10],\n", " 'model__reg_alpha': [0, 0.1, 1, 10]\n", "}\n", "\n", "# melakukan cross validation\n", "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n", "\n", "random_search_XGB = RandomizedSearchCV(\n", " pipeline, param_dist, n_iter=10, cv=kfold, verbose = 1, scoring='f1_macro', n_jobs=-1, random_state=42\n", ")\n", "random_search_XGB.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", random_search_XGB.best_params_)\n", "print(\"Best Cross-Validation f1 macro:\", random_search_XGB.best_score_)" ] }, { "cell_type": "markdown", "metadata": { "id": "808TBc-4Ieck" }, "source": [ "Setelah dilihat-lihat melakukan hyper parameter tuning mendapat nilai yang lebih rendah dibandingkan dengan yang original model." ] }, { "cell_type": "markdown", "metadata": { "id": "69SOoVzEUFFR" }, "source": [ "# FINAL MODEL" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "dMgfBTlWUD_T", "outputId": "ab6a3358-5b87-438e-f1b6-272dc8ab3334" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Terapkan SMOTE ke data training\n", "smote = SMOTE(random_state=42)\n", "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n", "\n", "# Latih model pada data yang sudah di-smote\n", "model = XGBClassifier()\n", "model.fit(X_train_resampled, y_train_resampled)\n", "\n", "# Evaluasi pada test set\n", "y_true = y_test\n", "y_pred = model.predict(X_test)\n", "y_pred_proba = model.predict_proba(X_test)\n", "\n", "\n", "class_labels = ['0','1']\n", "n_classes = len(class_labels)\n", "cm = confusion_matrix(y_true, y_pred)\n", "plt.figure(figsize=(8, 6))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_labels, yticklabels=class_labels)\n", "plt.title('Confusion Matrix')\n", "plt.ylabel('Actual Class')\n", "plt.xlabel('Predicted Class')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 859 }, "id": "gWIjcAzfUa9I", "outputId": "e8a9556c-1299-4f5b-f780-9a7af80cad50" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 0.62 0.62 0.62 8\n", " 1 0.94 0.94 0.94 54\n", "\n", " accuracy 0.90 62\n", " macro avg 0.78 0.78 0.78 62\n", "weighted avg 0.90 0.90 0.90 62\n", "\n", "--Evaluation Metrics--\n", "Accuracy : 0.9032258064516129\n", "Precision: 0.9444444444444444\n", "Recall : 0.9444444444444444\n", "F1 Score : 0.9444444444444444\n", "--ROC AUC Score--\n", "ROC AUC Score: 0.9282407407407407\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Evaluation Metrics\n", "print(\"Classification Report:\\n\", classification_report(y_true, y_pred, target_names=class_labels))\n", "print('--Evaluation Metrics--')\n", "print(\"Accuracy :\", accuracy_score(y_true, y_pred))\n", "print(\"Precision:\", precision_score(y_true, y_pred))\n", "print(\"Recall :\", recall_score(y_true, y_pred))\n", "print(\"F1 Score :\", f1_score(y_true, y_pred))\n", "\n", "# ROC AUC Score and Curve\n", "print('--ROC AUC Score--')\n", "roc_auc = roc_auc_score(y_true, y_pred_proba[:, 1])\n", "print(\"ROC AUC Score:\", roc_auc)\n", "\n", "# Hitung FPR, TPR\n", "fpr, tpr, thresholds = roc_curve(y_true, y_pred_proba[:, 1])\n", "\n", "# Plot ROC Curve\n", "plt.figure(figsize=(8, 6))\n", "plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')\n", "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--') # garis random\n", "plt.xlim([0.0, 1.0])\n", "plt.ylim([0.0, 1.05])\n", "plt.xlabel('False Positive Rate (1 - Specificity)')\n", "plt.ylabel('True Positive Rate (Sensitivity / Recall)')\n", "plt.title('ROC Curve - Binary Classification')\n", "plt.legend(loc=\"lower right\")\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "sxptYTKbJBOx" }, "source": [ "# Import Model" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "id": "hRF9g9gZJAUG" }, "outputs": [], "source": [ "import pickle\n", "with open('./model.pkl', 'wb') as f:\n", " pickle.dump(model, f)" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.12" } }, "nbformat": 4, "nbformat_minor": 0 }