{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "002ba07a", "metadata": {}, "outputs": [], "source": [ "\n", "# Import modules\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import re\n", "import numpy as np\n", "from sklearn import tree\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Figures inline and set visualization style\n", "%matplotlib inline\n", "sns.set()\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "2949d944", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(891, 12)\n" ] }, { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "\n", "df_train = pd.read_csv(\"C:/Users/User/Downloads/train.csv\")\n", "df_test = pd.read_csv(\"C:/Users/User/Downloads/test.csv\")\n", "\n", "\n", "print(df_train.shape)\n", "df_train.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "34b67c3b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(418, 11)\n" ] }, { "data": { "text/html": [ "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" ], "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 34.5 0 0 330911 7.8292 NaN Q \n", "1 47.0 1 0 363272 7.0000 NaN S \n", "2 62.0 0 0 240276 9.6875 NaN Q \n", "3 27.0 0 0 315154 8.6625 NaN S \n", "4 22.0 1 1 3101298 12.2875 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df_test.shape)\n", "df_test.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "4b1b1011", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', data=df_train);" ] }, { "cell_type": "code", "execution_count": 5, "id": "d60bf413", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Sex', data=df_train);" ] }, { "cell_type": "code", "execution_count": 6, "id": "1c2bfb25", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.catplot(x='Survived', col='Sex', kind='count', data=df_train);" ] }, { "cell_type": "code", "execution_count": 7, "id": "dbede04d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Sex\n", "female 233\n", "male 109\n", "Name: Survived, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.groupby(['Sex']).Survived.sum()" ] }, { "cell_type": "code", "execution_count": 8, "id": "eeeb3341", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "342" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.Survived.sum()" ] }, { "cell_type": "code", "execution_count": 9, "id": "937a3084", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7420382165605095\n", "0.18890814558058924\n" ] } ], "source": [ "print(df_train[df_train.Sex == 'female'].Survived.sum()/df_train[df_train.Sex == 'female'].Survived.count())\n", "print(df_train[df_train.Sex == 'male'].Survived.sum()/df_train[df_train.Sex == 'male'].Survived.count())" ] }, { "cell_type": "code", "execution_count": 10, "id": "864310ff", "metadata": {}, "outputs": [ { "data": { "image/png": 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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
41313053Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNS
41413061Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105C
41513073Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNS
41613083Ware, Mr. FrederickmaleNaN003593098.0500NaNS
41713093Peter, Master. Michael JmaleNaN11266822.3583NaNC
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" ], "text/plain": [ " PassengerId Pclass Name Sex Age SibSp \\\n", "413 1305 3 Spector, Mr. Woolf male NaN 0 \n", "414 1306 1 Oliva y Ocana, Dona. Fermina female 39.0 0 \n", "415 1307 3 Saether, Mr. Simon Sivertsen male 38.5 0 \n", "416 1308 3 Ware, Mr. Frederick male NaN 0 \n", "417 1309 3 Peter, Master. Michael J male NaN 1 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "413 0 A.5. 3236 8.0500 NaN S \n", "414 0 PC 17758 108.9000 C105 C \n", "415 0 SOTON/O.Q. 3101262 7.2500 NaN S \n", "416 0 359309 8.0500 NaN S \n", "417 1 2668 22.3583 NaN C " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Store target variable of training data in a safe place\n", "survived_train = df_train.Survived\n", "\n", "# Concatenate training and test sets\n", "data = pd.concat([df_train.drop(['Survived'], axis=1), df_test])\n", "data.tail()" ] }, { "cell_type": "code", "execution_count": 12, "id": "31431041", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
....................................
41313053Spector, Mr. WoolfmaleNaN00A.5. 32368.0500NaNS
41413061Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105C
41513073Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500NaNS
41613083Ware, Mr. FrederickmaleNaN003593098.0500NaNS
41713093Peter, Master. Michael JmaleNaN11266822.3583NaNC
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418 rows × 11 columns

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" ], "text/plain": [ " PassengerId Pclass Name \\\n", "0 892 3 Kelly, Mr. James \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) \n", "2 894 2 Myles, Mr. Thomas Francis \n", "3 895 3 Wirz, Mr. Albert \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) \n", ".. ... ... ... \n", "413 1305 3 Spector, Mr. Woolf \n", "414 1306 1 Oliva y Ocana, Dona. Fermina \n", "415 1307 3 Saether, Mr. Simon Sivertsen \n", "416 1308 3 Ware, Mr. Frederick \n", "417 1309 3 Peter, Master. Michael J \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 male 34.5 0 0 330911 7.8292 NaN Q \n", "1 female 47.0 1 0 363272 7.0000 NaN S \n", "2 male 62.0 0 0 240276 9.6875 NaN Q \n", "3 male 27.0 0 0 315154 8.6625 NaN S \n", "4 female 22.0 1 1 3101298 12.2875 NaN S \n", ".. ... ... ... ... ... ... ... ... \n", "413 male NaN 0 0 A.5. 3236 8.0500 NaN S \n", "414 female 39.0 0 0 PC 17758 108.9000 C105 C \n", "415 male 38.5 0 0 SOTON/O.Q. 3101262 7.2500 NaN S \n", "416 male NaN 0 0 359309 8.0500 NaN S \n", "417 male NaN 1 1 2668 22.3583 NaN C \n", "\n", "[418 rows x 11 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test" ] }, { "cell_type": "code", "execution_count": 13, "id": "ef626cce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 1\n", "2 1\n", "3 1\n", "4 0\n", " ..\n", "886 0\n", "887 1\n", "888 0\n", "889 1\n", "890 0\n", "Name: Survived, Length: 891, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survived_train" ] }, { "cell_type": "code", "execution_count": 14, "id": "d871eb29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1309 entries, 0 to 417\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 1309 non-null int64 \n", " 1 Pclass 1309 non-null int64 \n", " 2 Name 1309 non-null object \n", " 3 Sex 1309 non-null object \n", " 4 Age 1046 non-null float64\n", " 5 SibSp 1309 non-null int64 \n", " 6 Parch 1309 non-null int64 \n", " 7 Ticket 1309 non-null object \n", " 8 Fare 1308 non-null float64\n", " 9 Cabin 295 non-null object \n", " 10 Embarked 1307 non-null object \n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 122.7+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 15, "id": "7a39497b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 1309 entries, 0 to 417\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 1309 non-null int64 \n", " 1 Pclass 1309 non-null int64 \n", " 2 Name 1309 non-null object \n", " 3 Sex 1309 non-null object \n", " 4 Age 1309 non-null float64\n", " 5 SibSp 1309 non-null int64 \n", " 6 Parch 1309 non-null int64 \n", " 7 Ticket 1309 non-null object \n", " 8 Fare 1309 non-null float64\n", " 9 Cabin 295 non-null object \n", " 10 Embarked 1307 non-null object \n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 122.7+ KB\n" ] } ], "source": [ "# Dealing with missing numerical variables\n", "data['Age'] = data.Age.fillna(data.Age.median())\n", "data['Fare'] = data.Fare.fillna(data.Fare.median())\n", "\n", "# Check out info of data\n", "data.info()" ] }, { "cell_type": "code", "execution_count": 16, "id": "d6257a63", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdPclassNameAgeSibSpParchTicketFareCabinEmbarkedSex_male
013Braund, Mr. Owen Harris22.010A/5 211717.2500NaNS1
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.010PC 1759971.2833C85C0
233Heikkinen, Miss. Laina26.000STON/O2. 31012827.9250NaNS0
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01011380353.1000C123S0
453Allen, Mr. William Henry35.0003734508.0500NaNS1
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" ], "text/plain": [ " PassengerId Pclass Name \\\n", "0 1 3 Braund, Mr. Owen Harris \n", "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", "2 3 3 Heikkinen, Miss. Laina \n", "3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", "4 5 3 Allen, Mr. William Henry \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked Sex_male \n", "0 22.0 1 0 A/5 21171 7.2500 NaN S 1 \n", "1 38.0 1 0 PC 17599 71.2833 C85 C 0 \n", "2 26.0 0 0 STON/O2. 3101282 7.9250 NaN S 0 \n", "3 35.0 1 0 113803 53.1000 C123 S 0 \n", "4 35.0 0 0 373450 8.0500 NaN S 1 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.get_dummies(data, columns=['Sex'], drop_first=True)\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 17, "id": "fd501ce5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sex_maleFareAgePclassSibSp
017.250022.031
1071.283338.011
207.925026.030
3053.100035.011
418.050035.030
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" ], "text/plain": [ " Sex_male Fare Age Pclass SibSp\n", "0 1 7.2500 22.0 3 1\n", "1 0 71.2833 38.0 1 1\n", "2 0 7.9250 26.0 3 0\n", "3 0 53.1000 35.0 1 1\n", "4 1 8.0500 35.0 3 0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data[['Sex_male', 'Fare', 'Age','Pclass', 'SibSp']]\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 18, "id": "ac70fa2a", "metadata": {}, "outputs": [], "source": [ "data_train = data.iloc[:891]\n", "data_test = data.iloc[891:]" ] }, { "cell_type": "code", "execution_count": 19, "id": "f307ca52", "metadata": {}, "outputs": [], "source": [ "X = data_train.values\n", "test = data_test.values\n", "y = survived_train.values" ] }, { "cell_type": "code", "execution_count": 20, "id": "3c4de849", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
DecisionTreeClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "DecisionTreeClassifier()" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = tree.DecisionTreeClassifier()\n", "clf.fit(X, y)\n" ] }, { "cell_type": "code", "execution_count": 35, "id": "1e9b38a7", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "Tree\n", "\n", "\n", "\n", "0\n", "\n", "Sex_male ≤ 0.5\n", "gini = 0.473\n", "samples = 891\n", "value = [549, 342]\n", "class = Survived\n", "\n", "\n", "\n", "1\n", "\n", "Pclass ≤ 2.5\n", "gini = 0.383\n", "samples = 314\n", "value = [81, 233]\n", "class = Not Survived\n", "\n", "\n", "\n", "0->1\n", "\n", "\n", "True\n", "\n", "\n", "\n", "138\n", "\n", "Age ≤ 6.5\n", "gini = 0.306\n", "samples = 577\n", "value = [468, 109]\n", "class = Survived\n", "\n", "\n", "\n", "0->138\n", "\n", "\n", "False\n", "\n", "\n", "\n", "2\n", "\n", "Age ≤ 2.5\n", "gini = 0.1\n", "samples = 170\n", "value = [9, 161]\n", "class = Not Survived\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "\n", "\n", "\n", "45\n", "\n", "Fare ≤ 23.35\n", "gini = 0.5\n", "samples = 144\n", "value = [72, 72]\n", "class = Survived\n", "\n", "\n", "\n", "1->45\n", "\n", "\n", "\n", "\n", "\n", "3\n", "\n", "Pclass ≤ 1.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "2->3\n", "\n", "\n", "\n", "\n", "\n", "6\n", "\n", "Fare ≤ 28.856\n", "gini = 0.091\n", "samples = 168\n", "value = [8, 160]\n", "class = Not Survived\n", "\n", "\n", "\n", "2->6\n", "\n", "\n", "\n", "\n", "\n", "4\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "3->4\n", "\n", "\n", "\n", "\n", "\n", "5\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "3->5\n", "\n", "\n", "\n", "\n", "\n", "7\n", "\n", "Fare ≤ 28.231\n", "gini = 0.182\n", "samples = 69\n", "value = [7, 62]\n", "class = Not Survived\n", "\n", "\n", "\n", "6->7\n", "\n", "\n", "\n", "\n", "\n", "38\n", "\n", "Fare ≤ 149.035\n", "gini = 0.02\n", "samples = 99\n", "value = [1, 98]\n", "class = Not Survived\n", "\n", "\n", "\n", "6->38\n", "\n", "\n", "\n", "\n", "\n", "8\n", "\n", "Age ≤ 56.0\n", "gini = 0.161\n", "samples = 68\n", "value = [6, 62]\n", "class = Not Survived\n", "\n", "\n", "\n", "7->8\n", "\n", "\n", "\n", "\n", "\n", "37\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "7->37\n", "\n", "\n", "\n", "\n", "\n", "9\n", "\n", "SibSp ≤ 0.5\n", "gini = 0.14\n", "samples = 66\n", "value = [5, 61]\n", "class = Not Survived\n", "\n", "\n", "\n", "8->9\n", "\n", "\n", "\n", "\n", "\n", "34\n", "\n", "Pclass ≤ 1.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "8->34\n", "\n", "\n", "\n", "\n", "\n", "10\n", "\n", "Fare ≤ 13.25\n", "gini = 0.085\n", "samples = 45\n", "value = [2, 43]\n", "class = Not Survived\n", "\n", "\n", "\n", "9->10\n", "\n", "\n", "\n", "\n", "\n", "23\n", "\n", "Age ≤ 25.0\n", "gini = 0.245\n", "samples = 21\n", "value = [3, 18]\n", "class = Not Survived\n", "\n", "\n", "\n", "9->23\n", "\n", "\n", "\n", "\n", "\n", "11\n", "\n", "Fare ≤ 12.825\n", "gini = 0.147\n", "samples = 25\n", "value = [2, 23]\n", "class = Not Survived\n", "\n", "\n", "\n", "10->11\n", "\n", "\n", "\n", "\n", "\n", "22\n", "\n", "gini = 0.0\n", "samples = 20\n", "value = [0, 20]\n", "class = Not Survived\n", "\n", "\n", "\n", "10->22\n", "\n", "\n", "\n", "\n", "\n", "12\n", "\n", "gini = 0.0\n", "samples = 11\n", "value = [0, 11]\n", "class = Not Survived\n", "\n", "\n", "\n", "11->12\n", "\n", "\n", "\n", "\n", "\n", "13\n", "\n", "Age ≤ 26.0\n", "gini = 0.245\n", "samples = 14\n", "value = [2, 12]\n", "class = Not Survived\n", "\n", "\n", "\n", "11->13\n", "\n", "\n", "\n", "\n", "\n", "14\n", "\n", "Age ≤ 21.0\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "13->14\n", "\n", "\n", "\n", "\n", "\n", "17\n", "\n", "Age ≤ 37.0\n", "gini = 0.165\n", "samples = 11\n", "value = [1, 10]\n", "class = Not Survived\n", "\n", "\n", "\n", "13->17\n", "\n", "\n", "\n", "\n", "\n", "15\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "14->15\n", "\n", "\n", "\n", "\n", "\n", "16\n", "\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "14->16\n", "\n", "\n", "\n", "\n", "\n", "18\n", "\n", "gini = 0.0\n", "samples = 8\n", "value = [0, 8]\n", "class = Not Survived\n", "\n", "\n", "\n", "17->18\n", "\n", "\n", "\n", "\n", "\n", "19\n", "\n", "Age ≤ 39.0\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "17->19\n", "\n", "\n", "\n", "\n", "\n", "20\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "19->20\n", "\n", "\n", "\n", "\n", "\n", "21\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "19->21\n", "\n", "\n", "\n", "\n", "\n", "24\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [0, 6]\n", "class = Not Survived\n", "\n", "\n", "\n", "23->24\n", "\n", "\n", "\n", "\n", "\n", "25\n", "\n", "Age ≤ 27.5\n", "gini = 0.32\n", "samples = 15\n", "value = [3, 12]\n", "class = Not Survived\n", "\n", "\n", "\n", "23->25\n", "\n", "\n", "\n", "\n", "\n", "26\n", "\n", "Fare ≤ 17.429\n", "gini = 0.444\n", "samples = 3\n", "value = [2, 1]\n", "class = Survived\n", "\n", "\n", "\n", "25->26\n", "\n", "\n", "\n", "\n", "\n", "29\n", "\n", "Age ≤ 43.0\n", "gini = 0.153\n", "samples = 12\n", "value = [1, 11]\n", "class = Not Survived\n", "\n", "\n", "\n", "25->29\n", "\n", "\n", "\n", "\n", "\n", "27\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "26->27\n", "\n", "\n", "\n", "\n", "\n", "28\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "26->28\n", "\n", "\n", "\n", "\n", "\n", "30\n", "\n", "gini = 0.0\n", "samples = 9\n", "value = [0, 9]\n", "class = Not Survived\n", "\n", "\n", "\n", "29->30\n", "\n", "\n", "\n", "\n", "\n", "31\n", "\n", "Age ≤ 44.5\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "29->31\n", "\n", "\n", "\n", "\n", "\n", "32\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "31->32\n", "\n", "\n", "\n", "\n", "\n", "33\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "31->33\n", "\n", "\n", "\n", "\n", "\n", "35\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "34->35\n", "\n", "\n", "\n", "\n", "\n", "36\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "34->36\n", "\n", "\n", "\n", "\n", "\n", "39\n", "\n", "gini = 0.0\n", "samples = 81\n", "value = [0, 81]\n", "class = Not Survived\n", "\n", "\n", "\n", "38->39\n", "\n", "\n", "\n", "\n", "\n", "40\n", "\n", "Fare ≤ 152.506\n", "gini = 0.105\n", "samples = 18\n", "value = [1, 17]\n", "class = Not Survived\n", "\n", "\n", "\n", "38->40\n", "\n", "\n", "\n", "\n", "\n", "41\n", "\n", "Age ≤ 23.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "40->41\n", "\n", "\n", "\n", "\n", "\n", "44\n", "\n", "gini = 0.0\n", "samples = 16\n", "value = [0, 16]\n", "class = Not Survived\n", "\n", "\n", "\n", "40->44\n", "\n", "\n", "\n", "\n", "\n", "42\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "41->42\n", "\n", "\n", "\n", "\n", "\n", "43\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "41->43\n", "\n", "\n", "\n", "\n", "\n", "46\n", "\n", "Age ≤ 36.5\n", "gini = 0.484\n", "samples = 117\n", "value = [48, 69]\n", "class = Not Survived\n", "\n", "\n", "\n", "45->46\n", "\n", "\n", "\n", "\n", "\n", "125\n", "\n", "Fare ≤ 32.881\n", "gini = 0.198\n", "samples = 27\n", "value = [24, 3]\n", "class = Survived\n", "\n", "\n", "\n", "45->125\n", "\n", "\n", "\n", "\n", "\n", "47\n", "\n", "Fare ≤ 7.888\n", "gini = 0.472\n", "samples = 110\n", "value = [42, 68]\n", "class = Not Survived\n", "\n", "\n", "\n", "46->47\n", "\n", "\n", "\n", "\n", "\n", "122\n", "\n", "Age ≤ 55.0\n", "gini = 0.245\n", "samples = 7\n", "value = [6, 1]\n", "class = Survived\n", "\n", "\n", "\n", "46->122\n", "\n", "\n", "\n", "\n", "\n", "48\n", "\n", "Age ≤ 29.25\n", "gini = 0.393\n", "samples = 41\n", "value = [11, 30]\n", "class = Not Survived\n", "\n", "\n", "\n", "47->48\n", "\n", "\n", "\n", "\n", "\n", "77\n", "\n", "Fare ≤ 10.825\n", "gini = 0.495\n", "samples = 69\n", "value = [31, 38]\n", "class = Not Survived\n", "\n", "\n", "\n", "47->77\n", "\n", "\n", "\n", "\n", "\n", "49\n", "\n", "Fare ≤ 6.987\n", "gini = 0.355\n", "samples = 39\n", "value = [9, 30]\n", "class = Not Survived\n", "\n", "\n", "\n", "48->49\n", "\n", "\n", "\n", "\n", "\n", "76\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "48->76\n", "\n", "\n", "\n", "\n", "\n", "50\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "49->50\n", "\n", "\n", "\n", "\n", "\n", "51\n", "\n", "Fare ≤ 7.523\n", "gini = 0.332\n", "samples = 38\n", "value = [8, 30]\n", "class = Not Survived\n", "\n", "\n", "\n", "49->51\n", "\n", "\n", "\n", "\n", "\n", "52\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [0, 6]\n", "class = Not Survived\n", "\n", "\n", "\n", "51->52\n", "\n", "\n", "\n", "\n", "\n", "53\n", "\n", "Age ≤ 15.0\n", "gini = 0.375\n", "samples = 32\n", "value = [8, 24]\n", "class = Not Survived\n", "\n", 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"\n", "\n", "\n", "\n", "60\n", "\n", "Fare ≤ 7.763\n", "gini = 0.375\n", "samples = 20\n", "value = [5, 15]\n", "class = Not Survived\n", "\n", "\n", "\n", "59->60\n", "\n", "\n", "\n", "\n", "\n", "75\n", "\n", "gini = 0.0\n", "samples = 8\n", "value = [0, 8]\n", "class = Not Survived\n", "\n", "\n", "\n", "59->75\n", "\n", "\n", "\n", "\n", "\n", "61\n", "\n", "Fare ≤ 7.744\n", "gini = 0.291\n", "samples = 17\n", "value = [3, 14]\n", "class = Not Survived\n", "\n", "\n", "\n", "60->61\n", "\n", "\n", "\n", "\n", "\n", "70\n", "\n", "Age ≤ 20.0\n", "gini = 0.444\n", "samples = 3\n", "value = [2, 1]\n", "class = Survived\n", "\n", "\n", "\n", "60->70\n", "\n", "\n", "\n", "\n", "\n", "62\n", "\n", "gini = 0.0\n", "samples = 4\n", "value = [0, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "61->62\n", "\n", "\n", "\n", "\n", "\n", "63\n", "\n", "Age ≤ 21.5\n", "gini = 0.355\n", "samples = 13\n", "value = [3, 10]\n", "class = Not Survived\n", "\n", "\n", "\n", "61->63\n", "\n", "\n", "\n", "\n", "\n", "64\n", "\n", "Age ≤ 18.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "63->64\n", "\n", "\n", "\n", "\n", "\n", "67\n", "\n", "Age ≤ 25.0\n", "gini = 0.298\n", "samples = 11\n", "value = [2, 9]\n", "class = Not Survived\n", "\n", "\n", "\n", "63->67\n", "\n", "\n", "\n", "\n", "\n", "65\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "64->65\n", "\n", "\n", "\n", "\n", "\n", "66\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "64->66\n", "\n", "\n", "\n", "\n", "\n", "68\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "67->68\n", "\n", "\n", "\n", "\n", "\n", "69\n", "\n", "gini = 0.346\n", "samples = 9\n", "value = [2, 7]\n", "class = Not Survived\n", "\n", "\n", "\n", "67->69\n", "\n", "\n", "\n", "\n", "\n", "71\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "70->71\n", "\n", "\n", "\n", "\n", "\n", "72\n", "\n", "Age ≤ 23.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "70->72\n", "\n", "\n", "\n", "\n", "\n", "73\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "72->73\n", "\n", "\n", "\n", "\n", "\n", "74\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "72->74\n", "\n", "\n", "\n", "\n", "\n", "78\n", "\n", "Age ≤ 19.0\n", "gini = 0.434\n", "samples = 22\n", "value = [15, 7]\n", "class = Survived\n", "\n", "\n", "\n", "77->78\n", "\n", "\n", "\n", "\n", "\n", "91\n", "\n", "Fare ≤ 13.908\n", "gini = 0.449\n", "samples = 47\n", "value = [16, 31]\n", "class = Not Survived\n", "\n", "\n", "\n", "77->91\n", "\n", "\n", "\n", "\n", "\n", "79\n", "\n", "Fare ≤ 10.152\n", "gini = 0.32\n", "samples = 5\n", "value = [1, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "78->79\n", "\n", "\n", "\n", "\n", "\n", "82\n", "\n", "Age ≤ 30.5\n", "gini = 0.291\n", "samples = 17\n", "value = [14, 3]\n", "class = Survived\n", "\n", "\n", "\n", "78->82\n", "\n", "\n", "\n", "\n", "\n", "80\n", "\n", "gini = 0.0\n", "samples = 4\n", "value = [0, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "79->80\n", "\n", "\n", "\n", "\n", "\n", "81\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "79->81\n", "\n", "\n", "\n", "\n", "\n", "83\n", "\n", "Fare ≤ 7.988\n", "gini = 0.219\n", "samples = 16\n", "value = [14, 2]\n", "class = Survived\n", "\n", "\n", "\n", "82->83\n", "\n", "\n", "\n", "\n", "\n", "90\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "82->90\n", "\n", "\n", "\n", "\n", "\n", "84\n", "\n", "Age ≤ 25.5\n", "gini = 0.48\n", "samples = 5\n", "value = [3, 2]\n", "class = Survived\n", "\n", "\n", "\n", "83->84\n", "\n", "\n", "\n", "\n", "\n", "89\n", "\n", "gini = 0.0\n", "samples = 11\n", "value = [11, 0]\n", "class = Survived\n", "\n", "\n", "\n", "83->89\n", "\n", "\n", "\n", "\n", "\n", "85\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "84->85\n", "\n", "\n", "\n", "\n", "\n", "86\n", "\n", "Age ≤ 27.5\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "84->86\n", "\n", "\n", "\n", "\n", "\n", "87\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "86->87\n", "\n", "\n", "\n", "\n", "\n", "88\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "86->88\n", "\n", "\n", "\n", "\n", "\n", "92\n", "\n", "gini = 0.0\n", "samples = 8\n", "value = [0, 8]\n", "class = Not Survived\n", "\n", "\n", "\n", "91->92\n", "\n", "\n", "\n", "\n", "\n", "93\n", "\n", "Fare ≤ 15.373\n", "gini = 0.484\n", "samples = 39\n", "value = [16, 23]\n", "class = Not Survived\n", "\n", "\n", "\n", "91->93\n", "\n", "\n", "\n", "\n", "\n", "94\n", "\n", "Age ≤ 28.5\n", "gini = 0.32\n", "samples = 10\n", "value = [8, 2]\n", "class = Survived\n", "\n", "\n", "\n", "93->94\n", "\n", "\n", "\n", "\n", "\n", "101\n", "\n", "SibSp ≤ 2.5\n", "gini = 0.4\n", "samples = 29\n", "value = [8, 21]\n", "class = Not Survived\n", "\n", "\n", "\n", "93->101\n", "\n", "\n", "\n", "\n", "\n", "95\n", "\n", "Age ≤ 16.0\n", "gini = 0.198\n", "samples = 9\n", "value = [8, 1]\n", "class = Survived\n", "\n", "\n", "\n", "94->95\n", "\n", "\n", "\n", "\n", "\n", "100\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "94->100\n", "\n", "\n", "\n", "\n", "\n", "96\n", "\n", "Age ≤ 14.75\n", "gini = 0.444\n", "samples = 3\n", "value = [2, 1]\n", "class = Survived\n", "\n", "\n", "\n", "95->96\n", "\n", "\n", "\n", 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"\n", "Age ≤ 33.5\n", "gini = 0.5\n", "samples = 6\n", "value = [3, 3]\n", "class = Survived\n", "\n", "\n", "\n", "102->112\n", "\n", "\n", "\n", "\n", "\n", "104\n", "\n", "Age ≤ 11.5\n", "gini = 0.375\n", "samples = 12\n", "value = [3, 9]\n", "class = Not Survived\n", "\n", "\n", "\n", "103->104\n", "\n", "\n", "\n", "\n", "\n", "111\n", "\n", "gini = 0.0\n", "samples = 8\n", "value = [0, 8]\n", "class = Not Survived\n", "\n", "\n", "\n", "103->111\n", "\n", "\n", "\n", "\n", "\n", "105\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [0, 6]\n", "class = Not Survived\n", "\n", "\n", "\n", "104->105\n", "\n", "\n", "\n", "\n", "\n", "106\n", "\n", "SibSp ≤ 0.5\n", "gini = 0.5\n", "samples = 6\n", "value = [3, 3]\n", "class = Survived\n", "\n", "\n", "\n", "104->106\n", "\n", "\n", "\n", "\n", "\n", "107\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "106->107\n", "\n", "\n", "\n", "\n", "\n", "108\n", "\n", "Fare ≤ 15.975\n", "gini = 0.375\n", "samples = 4\n", "value = [3, 1]\n", "class = Survived\n", "\n", "\n", "\n", "106->108\n", "\n", "\n", "\n", "\n", "\n", "109\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "108->109\n", "\n", "\n", "\n", "\n", "\n", "110\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "108->110\n", "\n", "\n", "\n", "\n", "\n", "113\n", "\n", "Fare ≤ 19.262\n", "gini = 0.375\n", "samples = 4\n", "value = [3, 1]\n", "class = Survived\n", "\n", "\n", "\n", "112->113\n", "\n", "\n", "\n", "\n", "\n", "118\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "112->118\n", "\n", "\n", "\n", "\n", "\n", "114\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "113->114\n", "\n", "\n", "\n", "\n", "\n", "115\n", "\n", "SibSp ≤ 0.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "113->115\n", "\n", "\n", "\n", "\n", "\n", "116\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "115->116\n", "\n", "\n", "\n", "\n", "\n", "117\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "115->117\n", "\n", "\n", "\n", "\n", "\n", "120\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "119->120\n", "\n", "\n", "\n", "\n", "\n", "121\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "119->121\n", "\n", "\n", "\n", "\n", "\n", "123\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [6, 0]\n", "class = Survived\n", "\n", "\n", "\n", "122->123\n", "\n", "\n", "\n", "\n", "\n", "124\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", 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"SibSp ≤ 2.5\n", "gini = 0.444\n", "samples = 24\n", "value = [8, 16]\n", "class = Not Survived\n", "\n", "\n", "\n", "138->139\n", "\n", "\n", "\n", "\n", "\n", "146\n", "\n", "Pclass ≤ 1.5\n", "gini = 0.28\n", "samples = 553\n", "value = [460, 93]\n", "class = Survived\n", "\n", "\n", "\n", "138->146\n", "\n", "\n", "\n", "\n", "\n", "140\n", "\n", "gini = 0.0\n", "samples = 15\n", "value = [0, 15]\n", "class = Not Survived\n", "\n", "\n", "\n", "139->140\n", "\n", "\n", "\n", "\n", "\n", "141\n", "\n", "Age ≤ 2.5\n", "gini = 0.198\n", "samples = 9\n", "value = [8, 1]\n", "class = Survived\n", "\n", "\n", "\n", "139->141\n", "\n", "\n", "\n", "\n", "\n", "142\n", "\n", "gini = 0.0\n", "samples = 5\n", "value = [5, 0]\n", "class = Survived\n", "\n", "\n", "\n", "141->142\n", "\n", "\n", "\n", "\n", "\n", "143\n", "\n", "Age ≤ 3.5\n", "gini = 0.375\n", "samples = 4\n", "value = [3, 1]\n", "class = Survived\n", "\n", "\n", "\n", "141->143\n", "\n", "\n", "\n", "\n", "\n", "144\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "143->144\n", "\n", "\n", "\n", "\n", "\n", "145\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "143->145\n", "\n", "\n", "\n", "\n", "\n", "147\n", "\n", "Fare ≤ 26.144\n", "gini = 0.46\n", "samples = 120\n", "value = [77, 43]\n", "class = Survived\n", "\n", "\n", "\n", "146->147\n", "\n", "\n", "\n", "\n", "\n", "226\n", "\n", "Fare ≤ 51.698\n", "gini = 0.204\n", "samples = 433\n", "value = [383, 50]\n", "class = Survived\n", "\n", "\n", "\n", "146->226\n", "\n", "\n", "\n", "\n", "\n", "148\n", "\n", "gini = 0.0\n", "samples = 10\n", "value = [10, 0]\n", "class = Survived\n", "\n", "\n", "\n", "147->148\n", "\n", "\n", "\n", "\n", "\n", "149\n", "\n", "Age ≤ 53.0\n", "gini = 0.476\n", "samples = 110\n", "value = [67, 43]\n", "class = Survived\n", "\n", "\n", "\n", "147->149\n", "\n", "\n", "\n", "\n", "\n", "150\n", "\n", "Fare ≤ 27.135\n", "gini = 0.495\n", "samples = 89\n", "value = [49, 40]\n", "class = Survived\n", "\n", "\n", "\n", "149->150\n", "\n", "\n", "\n", "\n", "\n", "215\n", "\n", "Age ≤ 75.5\n", "gini = 0.245\n", "samples = 21\n", "value = [18, 3]\n", "class = Survived\n", "\n", "\n", "\n", "149->215\n", "\n", "\n", "\n", "\n", "\n", "151\n", "\n", "Fare ≤ 26.469\n", "gini = 0.26\n", "samples = 13\n", "value = [2, 11]\n", "class = Not Survived\n", "\n", "\n", "\n", "150->151\n", "\n", "\n", "\n", "\n", "\n", "160\n", "\n", "Fare ≤ 387.665\n", "gini = 0.472\n", "samples = 76\n", "value = [47, 29]\n", "class = Survived\n", "\n", "\n", "\n", "150->160\n", "\n", "\n", "\n", "\n", "\n", "152\n", "\n", "gini = 0.0\n", "samples = 4\n", "value = [0, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "151->152\n", "\n", "\n", "\n", "\n", "\n", "153\n", "\n", "Age ≤ 46.5\n", "gini = 0.346\n", "samples = 9\n", "value = [2, 7]\n", "class = Not Survived\n", "\n", "\n", "\n", "151->153\n", "\n", "\n", "\n", "\n", "\n", "154\n", "\n", "Age ≤ 40.0\n", "gini = 0.408\n", "samples = 7\n", "value = [2, 5]\n", "class = Not Survived\n", "\n", "\n", "\n", "153->154\n", "\n", "\n", "\n", "\n", "\n", "159\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "153->159\n", "\n", "\n", "\n", "\n", "\n", "155\n", "\n", "Age ≤ 31.0\n", "gini = 0.32\n", "samples = 5\n", "value = [1, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "154->155\n", "\n", "\n", "\n", "\n", "\n", "158\n", "\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "154->158\n", "\n", "\n", "\n", "\n", "\n", "156\n", "\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "155->156\n", "\n", "\n", "\n", "\n", "\n", "157\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "155->157\n", "\n", "\n", "\n", "\n", "\n", "161\n", "\n", "Fare ≤ 134.642\n", "gini = 0.463\n", "samples = 74\n", "value = [47, 27]\n", "class = Survived\n", "\n", "\n", "\n", "160->161\n", "\n", "\n", "\n", "\n", "\n", "214\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "160->214\n", "\n", "\n", "\n", "\n", "\n", "162\n", "\n", "Age ≤ 27.5\n", "gini = 0.481\n", "samples = 67\n", "value = [40, 27]\n", "class = Survived\n", "\n", "\n", "\n", "161->162\n", "\n", "\n", "\n", "\n", "\n", "213\n", "\n", "gini = 0.0\n", "samples = 7\n", "value = [7, 0]\n", "class = Survived\n", "\n", "\n", "\n", "161->213\n", "\n", "\n", "\n", "\n", "\n", "163\n", "\n", "Age ≤ 24.5\n", "gini = 0.426\n", "samples = 13\n", "value = [4, 9]\n", "class = Not Survived\n", "\n", "\n", "\n", "162->163\n", "\n", "\n", "\n", "\n", "\n", "172\n", "\n", "Fare ≤ 115.442\n", "gini = 0.444\n", "samples = 54\n", "value = [36, 18]\n", "class = Survived\n", "\n", "\n", "\n", "162->172\n", "\n", "\n", "\n", "\n", "\n", "164\n", "\n", "Fare ≤ 109.892\n", "gini = 0.49\n", "samples = 7\n", "value = [4, 3]\n", "class = Survived\n", "\n", "\n", "\n", "163->164\n", "\n", "\n", "\n", "\n", "\n", "171\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [0, 6]\n", "class = Not Survived\n", "\n", "\n", "\n", "163->171\n", "\n", "\n", "\n", "\n", "\n", "165\n", "\n", "Age ≤ 22.0\n", "gini = 0.32\n", "samples = 5\n", "value = [4, 1]\n", "class = Survived\n", "\n", "\n", "\n", "164->165\n", "\n", "\n", "\n", "\n", "\n", "170\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "164->170\n", "\n", "\n", "\n", "\n", "\n", "166\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "165->166\n", "\n", "\n", "\n", "\n", "\n", "167\n", "\n", "Fare ≤ 71.279\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "165->167\n", "\n", "\n", "\n", "\n", "\n", "168\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "167->168\n", "\n", "\n", "\n", "\n", "\n", "169\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "167->169\n", "\n", "\n", "\n", "\n", "\n", "173\n", "\n", "Fare ≤ 29.1\n", "gini = 0.426\n", "samples = 52\n", "value = [36, 16]\n", "class = Survived\n", "\n", "\n", "\n", "172->173\n", "\n", "\n", "\n", "\n", "\n", "212\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "172->212\n", "\n", "\n", "\n", "\n", "\n", "174\n", "\n", "gini = 0.0\n", "samples = 5\n", "value = [5, 0]\n", "class = Survived\n", "\n", "\n", "\n", "173->174\n", "\n", "\n", "\n", "\n", "\n", "175\n", "\n", "Fare ≤ 30.598\n", "gini = 0.449\n", "samples = 47\n", "value = [31, 16]\n", "class = Survived\n", "\n", "\n", "\n", "173->175\n", "\n", "\n", "\n", "\n", "\n", "176\n", "\n", "Age ≤ 28.5\n", "gini = 0.408\n", "samples = 7\n", "value = [2, 5]\n", "class = Not Survived\n", "\n", "\n", "\n", "175->176\n", "\n", "\n", "\n", "\n", "\n", "181\n", "\n", "SibSp ≤ 0.5\n", "gini = 0.399\n", "samples = 40\n", "value = [29, 11]\n", "class = Survived\n", "\n", "\n", "\n", "175->181\n", "\n", "\n", "\n", "\n", "\n", "177\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [0, 3]\n", "class = Not Survived\n", "\n", "\n", "\n", "176->177\n", "\n", "\n", "\n", "\n", "\n", "178\n", "\n", "Fare ≤ 30.25\n", "gini = 0.5\n", "samples = 4\n", "value = [2, 2]\n", "class = Survived\n", "\n", "\n", "\n", "176->178\n", "\n", "\n", "\n", "\n", "\n", "179\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "178->179\n", "\n", "\n", "\n", "\n", "\n", "180\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "178->180\n", "\n", "\n", "\n", "\n", "\n", "182\n", "\n", "Fare ≤ 37.0\n", "gini = 0.266\n", "samples = 19\n", "value = [16, 3]\n", "class = Survived\n", "\n", "\n", "\n", "181->182\n", "\n", "\n", "\n", "\n", "\n", "193\n", "\n", "Age ≤ 49.5\n", "gini = 0.472\n", "samples = 21\n", "value = [13, 8]\n", "class = Survived\n", "\n", "\n", "\n", "181->193\n", "\n", "\n", "\n", "\n", "\n", "183\n", "\n", "Fare ≤ 35.25\n", "gini = 0.469\n", "samples = 8\n", "value = [5, 3]\n", "class = Survived\n", "\n", "\n", "\n", "182->183\n", "\n", "\n", "\n", "\n", "\n", "192\n", "\n", "gini = 0.0\n", "samples = 11\n", "value = [11, 0]\n", "class = Survived\n", "\n", "\n", "\n", "182->192\n", "\n", "\n", "\n", "\n", "\n", "184\n", "\n", "Age ≤ 34.0\n", "gini = 0.32\n", "samples = 5\n", "value = [4, 1]\n", "class = Survived\n", "\n", "\n", "\n", "183->184\n", "\n", "\n", "\n", "\n", "\n", "189\n", "\n", "Age ≤ 36.5\n", "gini = 0.444\n", "samples = 3\n", "value = [1, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "183->189\n", "\n", "\n", "\n", "\n", "\n", "185\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "184->185\n", "\n", "\n", "\n", "\n", "\n", "186\n", "\n", "Age ≤ 43.5\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "184->186\n", "\n", "\n", "\n", "\n", "\n", "187\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "186->187\n", "\n", "\n", "\n", "\n", "\n", "188\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "186->188\n", "\n", "\n", "\n", "\n", "\n", "190\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "189->190\n", "\n", "\n", "\n", "\n", "\n", "191\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "189->191\n", "\n", "\n", "\n", "\n", "\n", "194\n", "\n", "Age ≤ 47.0\n", "gini = 0.494\n", "samples = 18\n", "value = [10, 8]\n", "class = Survived\n", "\n", "\n", "\n", "193->194\n", "\n", "\n", "\n", "\n", "\n", "211\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "193->211\n", "\n", "\n", "\n", "\n", "\n", "195\n", "\n", "Fare ≤ 59.087\n", "gini = 0.426\n", "samples = 13\n", "value = [9, 4]\n", "class = Survived\n", "\n", "\n", "\n", "194->195\n", "\n", "\n", "\n", "\n", "\n", "208\n", "\n", "Fare ≤ 99.994\n", "gini = 0.32\n", "samples = 5\n", "value = [1, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "194->208\n", "\n", "\n", "\n", "\n", "\n", "196\n", "\n", "Fare ≤ 52.277\n", "gini = 0.5\n", "samples = 6\n", "value = [3, 3]\n", "class = Survived\n", "\n", "\n", "\n", "195->196\n", "\n", "\n", "\n", "\n", "\n", "203\n", "\n", "Fare ≤ 86.737\n", "gini = 0.245\n", "samples = 7\n", "value = [6, 1]\n", "class = Survived\n", "\n", "\n", "\n", "195->203\n", "\n", "\n", "\n", "\n", "\n", "197\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "196->197\n", "\n", "\n", "\n", "\n", "\n", "198\n", "\n", "Fare ≤ 52.827\n", "gini = 0.375\n", "samples = 4\n", "value = [1, 3]\n", "class = Not Survived\n", "\n", "\n", "\n", "196->198\n", "\n", "\n", "\n", "\n", "\n", "199\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "198->199\n", "\n", "\n", "\n", "\n", "\n", "200\n", "\n", "Age ≤ 34.0\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "198->200\n", "\n", "\n", "\n", "\n", "\n", "201\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "200->201\n", "\n", "\n", "\n", "\n", "\n", "202\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "200->202\n", "\n", "\n", "\n", "\n", "\n", "204\n", "\n", "gini = 0.0\n", "samples = 5\n", "value = [5, 0]\n", "class = Survived\n", "\n", "\n", "\n", "203->204\n", "\n", "\n", "\n", "\n", "\n", "205\n", "\n", "Age ≤ 41.0\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "203->205\n", "\n", "\n", "\n", "\n", "\n", "206\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "205->206\n", "\n", "\n", "\n", "\n", "\n", "207\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "205->207\n", "\n", "\n", "\n", "\n", "\n", "209\n", "\n", "gini = 0.0\n", "samples = 4\n", "value = [0, 4]\n", "class = Not Survived\n", "\n", "\n", "\n", "208->209\n", "\n", "\n", "\n", "\n", "\n", "210\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "208->210\n", "\n", "\n", "\n", "\n", "\n", "216\n", "\n", "Fare ≤ 35.077\n", "gini = 0.18\n", "samples = 20\n", "value = [18, 2]\n", "class = Survived\n", "\n", "\n", "\n", "215->216\n", "\n", "\n", "\n", "\n", "\n", "225\n", "\n", "gini = 0.0\n", 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"value = [2, 1]\n", "class = Survived\n", "\n", "\n", "\n", "220->222\n", "\n", "\n", "\n", "\n", "\n", "223\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "222->223\n", "\n", "\n", "\n", "\n", "\n", "224\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "222->224\n", "\n", "\n", "\n", "\n", "\n", "227\n", "\n", "Age ≤ 13.0\n", "gini = 0.193\n", "samples = 417\n", "value = [372, 45]\n", "class = Survived\n", "\n", "\n", "\n", "226->227\n", "\n", "\n", "\n", "\n", "\n", "404\n", "\n", "Fare ≤ 63.023\n", "gini = 0.43\n", "samples = 16\n", "value = [11, 5]\n", "class = Survived\n", "\n", "\n", "\n", "226->404\n", "\n", "\n", "\n", "\n", "\n", "228\n", "\n", "SibSp ≤ 2.0\n", "gini = 0.444\n", "samples = 12\n", "value = [8, 4]\n", "class = Survived\n", "\n", "\n", "\n", "227->228\n", "\n", "\n", "\n", "\n", "\n", "235\n", "\n", "Age ≤ 32.25\n", "gini = 0.182\n", "samples = 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17\n", "value = [17, 0]\n", "class = Survived\n", "\n", "\n", "\n", "237->355\n", "\n", "\n", "\n", "\n", "\n", "239\n", "\n", "Age ≤ 28.75\n", "gini = 0.19\n", "samples = 264\n", "value = [236, 28]\n", "class = Survived\n", "\n", "\n", "\n", "238->239\n", "\n", "\n", "\n", "\n", "\n", "354\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "238->354\n", "\n", "\n", "\n", "\n", "\n", "240\n", "\n", "Fare ≤ 15.173\n", "gini = 0.179\n", "samples = 241\n", "value = [217, 24]\n", "class = Survived\n", "\n", "\n", "\n", "239->240\n", "\n", "\n", "\n", "\n", "\n", "339\n", "\n", "Fare ≤ 10.0\n", "gini = 0.287\n", "samples = 23\n", "value = [19, 4]\n", "class = Survived\n", "\n", "\n", "\n", "239->339\n", "\n", "\n", "\n", "\n", "\n", "241\n", "\n", "Fare ≤ 7.798\n", "gini = 0.165\n", "samples = 220\n", "value = [200, 20]\n", "class = Survived\n", "\n", "\n", "\n", "240->241\n", "\n", "\n", "\n", "\n", "\n", "328\n", "\n", "Fare ≤ 15.373\n", "gini = 0.308\n", "samples = 21\n", "value = [17, 4]\n", "class = Survived\n", "\n", "\n", "\n", "240->328\n", "\n", "\n", "\n", "\n", "\n", "242\n", "\n", "Fare ≤ 7.763\n", "gini = 0.215\n", "samples = 90\n", "value = [79, 11]\n", "class = Survived\n", "\n", "\n", "\n", "241->242\n", "\n", "\n", "\n", "\n", "\n", "289\n", "\n", "Age ≤ 20.5\n", "gini = 0.129\n", "samples = 130\n", "value = [121, 9]\n", "class = Survived\n", "\n", "\n", "\n", "241->289\n", "\n", "\n", "\n", "\n", "\n", "243\n", "\n", "Fare ≤ 7.24\n", "gini = 0.169\n", "samples = 75\n", "value = [68, 7]\n", "class = Survived\n", "\n", "\n", "\n", "242->243\n", "\n", "\n", "\n", "\n", "\n", "274\n", "\n", "Age ≤ 20.0\n", "gini = 0.391\n", "samples = 15\n", "value = [11, 4]\n", "class = Survived\n", "\n", "\n", "\n", "242->274\n", "\n", "\n", "\n", "\n", "\n", "244\n", "\n", "Age ≤ 27.5\n", "gini = 0.245\n", "samples = 42\n", "value = [36, 6]\n", "class = Survived\n", "\n", "\n", "\n", "243->244\n", "\n", "\n", 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0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "257->259\n", "\n", "\n", "\n", "\n", "\n", "263\n", "\n", "gini = 0.0\n", "samples = 14\n", "value = [14, 0]\n", "class = Survived\n", "\n", "\n", "\n", "262->263\n", "\n", "\n", "\n", "\n", "\n", "264\n", "\n", "Age ≤ 28.25\n", "gini = 0.245\n", "samples = 7\n", "value = [6, 1]\n", "class = Survived\n", "\n", "\n", "\n", "262->264\n", "\n", "\n", "\n", "\n", "\n", "265\n", "\n", "gini = 0.278\n", "samples = 6\n", "value = [5, 1]\n", "class = Survived\n", "\n", "\n", "\n", "264->265\n", "\n", "\n", "\n", "\n", "\n", "266\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "264->266\n", "\n", "\n", "\n", "\n", "\n", "268\n", "\n", "gini = 0.0\n", "samples = 21\n", "value = [21, 0]\n", "class = Survived\n", "\n", "\n", "\n", "267->268\n", "\n", "\n", "\n", "\n", "\n", "269\n", "\n", "Age ≤ 23.0\n", "gini = 0.153\n", "samples = 12\n", "value = [11, 1]\n", "class = Survived\n", "\n", "\n", "\n", "267->269\n", "\n", "\n", "\n", "\n", "\n", "270\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "269->270\n", "\n", "\n", "\n", "\n", "\n", "271\n", "\n", "SibSp ≤ 0.5\n", "gini = 0.165\n", "samples = 11\n", "value = [10, 1]\n", "class = Survived\n", "\n", "\n", "\n", "269->271\n", "\n", "\n", "\n", "\n", "\n", "272\n", "\n", "gini = 0.18\n", "samples = 10\n", "value = [9, 1]\n", "class = Survived\n", "\n", "\n", "\n", "271->272\n", "\n", "\n", "\n", "\n", "\n", "273\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [1, 0]\n", "class = Survived\n", "\n", "\n", "\n", "271->273\n", "\n", "\n", "\n", "\n", "\n", "275\n", "\n", "gini = 0.0\n", "samples = 4\n", "value = [4, 0]\n", "class = Survived\n", "\n", "\n", "\n", "274->275\n", "\n", "\n", "\n", "\n", "\n", "276\n", "\n", "Age ≤ 26.5\n", "gini = 0.463\n", "samples = 11\n", "value = [7, 4]\n", "class = Survived\n", "\n", "\n", "\n", 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= 35\n", "value = [32, 3]\n", "class = Survived\n", "\n", "\n", "\n", "386->387\n", "\n", "\n", "\n", "\n", "\n", "400\n", "\n", "gini = 0.0\n", "samples = 31\n", "value = [31, 0]\n", "class = Survived\n", "\n", "\n", "\n", "386->400\n", "\n", "\n", "\n", "\n", "\n", "388\n", "\n", "Age ≤ 44.5\n", "gini = 0.08\n", "samples = 24\n", "value = [23, 1]\n", "class = Survived\n", "\n", "\n", "\n", "387->388\n", "\n", "\n", "\n", "\n", "\n", "393\n", "\n", "Age ≤ 45.0\n", "gini = 0.298\n", "samples = 11\n", "value = [9, 2]\n", "class = Survived\n", "\n", "\n", "\n", "387->393\n", "\n", "\n", "\n", "\n", "\n", "389\n", "\n", "gini = 0.0\n", "samples = 17\n", "value = [17, 0]\n", "class = Survived\n", "\n", "\n", "\n", "388->389\n", "\n", "\n", "\n", "\n", "\n", "390\n", "\n", "Age ≤ 46.0\n", "gini = 0.245\n", "samples = 7\n", "value = [6, 1]\n", "class = Survived\n", "\n", "\n", "\n", "388->390\n", "\n", "\n", "\n", "\n", "\n", "391\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "390->391\n", "\n", "\n", "\n", "\n", "\n", "392\n", "\n", "gini = 0.0\n", "samples = 6\n", "value = [6, 0]\n", "class = Survived\n", "\n", "\n", "\n", "390->392\n", "\n", "\n", "\n", "\n", "\n", "394\n", "\n", "Age ≤ 40.5\n", "gini = 0.375\n", "samples = 8\n", "value = [6, 2]\n", "class = Survived\n", "\n", "\n", "\n", "393->394\n", "\n", "\n", "\n", "\n", "\n", "399\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "393->399\n", "\n", "\n", "\n", "\n", "\n", "395\n", "\n", "Age ≤ 35.0\n", "gini = 0.278\n", "samples = 6\n", "value = [5, 1]\n", "class = Survived\n", "\n", "\n", "\n", "394->395\n", "\n", "\n", "\n", "\n", "\n", "398\n", "\n", "gini = 0.5\n", "samples = 2\n", "value = [1, 1]\n", "class = Survived\n", "\n", "\n", "\n", "394->398\n", "\n", "\n", "\n", "\n", "\n", "396\n", "\n", "gini = 0.444\n", "samples = 3\n", "value = [2, 1]\n", "class = Survived\n", "\n", "\n", "\n", "395->396\n", "\n", "\n", "\n", "\n", "\n", "397\n", "\n", "gini = 0.0\n", "samples = 3\n", "value = [3, 0]\n", "class = Survived\n", "\n", "\n", "\n", "395->397\n", "\n", "\n", "\n", "\n", "\n", "402\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "401->402\n", "\n", "\n", "\n", "\n", "\n", "403\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [2, 0]\n", "class = Survived\n", "\n", "\n", "\n", "401->403\n", "\n", "\n", "\n", "\n", "\n", "405\n", "\n", "Age ≤ 30.0\n", "gini = 0.408\n", "samples = 7\n", "value = [2, 5]\n", "class = Not Survived\n", "\n", "\n", "\n", "404->405\n", "\n", "\n", "\n", "\n", "\n", "410\n", "\n", "gini = 0.0\n", "samples = 9\n", "value = [9, 0]\n", "class = Survived\n", "\n", "\n", "\n", "404->410\n", "\n", "\n", "\n", "\n", "\n", "406\n", "\n", "Age ≤ 27.0\n", "gini = 0.48\n", "samples = 5\n", "value = [2, 3]\n", "class = Not Survived\n", "\n", "\n", "\n", "405->406\n", "\n", "\n", "\n", "\n", "\n", "409\n", "\n", "gini = 0.0\n", "samples = 2\n", "value = [0, 2]\n", "class = Not Survived\n", "\n", "\n", "\n", "405->409\n", "\n", "\n", "\n", "\n", "\n", "407\n", "\n", "gini = 0.0\n", "samples = 1\n", "value = [0, 1]\n", "class = Not Survived\n", "\n", "\n", "\n", "406->407\n", "\n", "\n", "\n", "\n", "\n", "408\n", "\n", "gini = 0.5\n", "samples = 4\n", "value = [2, 2]\n", "class = Survived\n", "\n", "\n", "\n", "406->408\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import graphviz \n", "from PIL import Image\n", "dot_data = tree.export_graphviz(clf, out_file=None) \n", "graph = graphviz.Source(dot_data) \n", "graph.render(\"Titanic\") \n", "\n", "dot_data = tree.export_graphviz(clf, out_file=None, \n", " feature_names=data_train.columns.values, \n", " class_names=['Survived','Not Survived'], \n", " filled=True, rounded=True, \n", " special_characters=True) \n", "graph = graphviz.Source(dot_data) \n", "graph " ] }, { "cell_type": "code", "execution_count": 22, "id": "8ad7b604", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.978675645342312" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Make predictions and store in 'Survived' column of df_test\n", "Y_pred = clf.predict(test)\n", "df_test['Survived'] = Y_pred\n", "clf.score(X, y)" ] }, { "cell_type": "code", "execution_count": 23, "id": "0d0f2be2", "metadata": {}, "outputs": [], "source": [ "df_test[['PassengerId', 'Survived']].to_csv('C:/Users/User/Desktop/dt.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 34, "id": "ae505ffa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Source.gv.pdf'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "src = graphviz.Source(dot_data)\n", "src.view()" ] }, { "cell_type": "code", "execution_count": null, "id": "e046624a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "ml", "language": "python", "name": "ml" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }