diff --git "a/GA_KKPM.ipynb" "b/GA_KKPM.ipynb" --- "a/GA_KKPM.ipynb" +++ "b/GA_KKPM.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": { "id": "l8Y_Fz5_VKUf" }, @@ -19,25 +19,16 @@ "warnings.filterwarnings(\"ignore\")\n", "\n", "from sklearn.model_selection import train_test_split\n", + "from sklearn import tree\n", "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OIhtQD8eWLMb" - }, - "outputs": [], - "source": [ - "# from google.colab import drive\n", - "# drive.mount('/content/drive')" + "from sklearn.metrics import accuracy_score\n", + "\n", + "import graphviz" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "metadata": { "id": "mIqh1kxmVQ9o" }, @@ -53,7 +44,6 @@ "\n", "def acc_score(df,label):\n", " score = pd.DataFrame({\"Classifier\":classifiers})\n", - " j = 0 # bisa jadi ngga dipake\n", " acc = []\n", " X_train,X_test,Y_train,Y_test = split(df,label)\n", " for i in models:\n", @@ -61,7 +51,6 @@ " model.fit(X_train,Y_train)\n", " predictions = model.predict(X_test)\n", " acc.append(accuracy_score(Y_test,predictions))\n", - " j = j+1 # bisa jadi ngga dipake\n", " score[\"Accuracy\"] = acc\n", " score.sort_values(by=\"Accuracy\", ascending=False,inplace = True)\n", " score.reset_index(drop=True, inplace=True)\n", @@ -72,13 +61,12 @@ " plt.figure(figsize=(6,4))\n", " ax = sns.pointplot(x=gen, y=score,color = c )\n", " ax.set(xlabel=\"Generation\", ylabel=\"Accuracy\")\n", - " # ax.set(ylim=(x,y))\n", " plt.show()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": { "id": "SYWqktBJVQ7I" }, @@ -98,7 +86,7 @@ " models = []\n", " for chromosome in population:\n", " logmodel = DecisionTreeClassifier(random_state=0)\n", - " logmodel.fit(X_train.iloc[:,chromosome],Y_train)\n", + " logmodel.fit(X_train.iloc[:,chromosome], Y_train)\n", " predictions = logmodel.predict(X_test.iloc[:,chromosome])\n", " scores.append(accuracy_score(Y_test,predictions))\n", " models.append(logmodel)\n", @@ -106,7 +94,7 @@ " inds = np.argsort(scores)\n", " return list(scores[inds][::-1]), list(population[inds,:][::-1]), list(models[inds][::-1])\n", "\n", - "def selection(pop_after_fit,n_parents):\n", + "def selection(pop_after_fit, n_parents):\n", " population_nextgen = []\n", " for i in range(n_parents):\n", " population_nextgen.append(pop_after_fit[i])\n", @@ -123,8 +111,7 @@ "\n", "def mutation(pop_after_cross, mutation_rate, n_feat):\n", " mutation_range = int(mutation_rate * n_feat)\n", - " pop_next_gen = []\n", - " for n in range(0, len(pop_after_cross)):\n", + " for n in range(64, len(pop_after_cross)):\n", " chromo = pop_after_cross[n]\n", " rand_posi = []\n", " for i in range(0, mutation_range):\n", @@ -132,18 +119,17 @@ " rand_posi.append(pos)\n", " for j in rand_posi:\n", " chromo[j] = not chromo[j]\n", - " pop_next_gen.append(chromo)\n", - " return pop_next_gen\n", + " pop_after_cross[n] = chromo\n", + " return pop_after_cross\n", "\n", - "def generations(df,label,size,n_feat,n_parents,mutation_rate,n_gen,X_train,\n", - " X_test, Y_train, Y_test):\n", + "def generations(df, label, size, n_feat, n_parents, mutation_rate, n_gen, X_train, X_test, Y_train, Y_test):\n", " best_chromo = []\n", " best_score = []\n", " best_models = []\n", " population_nextgen=initilization_of_population(size,n_feat)\n", " for i in range(n_gen):\n", " scores, pop_after_fit, models = fitness_score(population_nextgen)\n", - " print('Best score in generation',i+1,':',scores[:1]) #2\n", + " print('Best score in generation',i+1,':',scores[:1])\n", "\n", " pop_after_sel = selection(pop_after_fit, n_parents)\n", " pop_after_cross = crossover(pop_after_sel)\n", @@ -158,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -370,7 +356,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 4, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -384,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -596,7 +582,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 5, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -608,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -627,7 +613,7 @@ " dtype='object')" ] }, - "execution_count": 6, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -638,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1005,7 +991,7 @@ "[920 rows x 22 columns]" ] }, - "execution_count": 7, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1016,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1066,20 +1052,19 @@ "0 DecisionTree 0.717391" ] }, - "execution_count": 8, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# harusnya jangan sampai 100%\n", "score1 = acc_score(data_hd.iloc[:, :-1], data_hd['num'])\n", "score1" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1093,11 +1078,11 @@ "output_type": "stream", "text": [ "(690, 22) (230, 22) (690,) (230,)\n", - "Best score in generation 1 : [0.7913043478260869]\n", - "Best score in generation 2 : [0.7913043478260869]\n", - "Best score in generation 3 : [0.8173913043478261]\n", - "Best score in generation 4 : [0.8130434782608695]\n", - "Best score in generation 5 : [0.8260869565217391]\n" + "Best score in generation 1 : [0.8]\n", + "Best score in generation 2 : [0.808695652173913]\n", + "Best score in generation 3 : [0.8130434782608695]\n", + "Best score in generation 4 : [0.8217391304347826]\n", + "Best score in generation 5 : [0.8217391304347826]\n" ] } ], @@ -1106,7 +1091,7 @@ "print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)\n", "chromo_df, score, best_models = generations(data_hd.iloc[:, :-1],\n", " data_hd['num'],\n", - " size=80,\n", + " size=96,\n", " n_feat = data_hd.iloc[:, :-1].shape[1],\n", " n_parents=64,\n", " mutation_rate=0.20,\n", @@ -1119,7 +1104,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1131,7 +1116,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1146,19 +1131,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": { "id": "HQrzrFeuz0yG" }, "outputs": [], "source": [ - "# for index, clf in enumerate(best_models):\n", - "# dump(clf, 'model-{}.joblib'.format(index))" + "for index, clf in enumerate(best_models):\n", + " dump(clf, 'model-{}.joblib'.format(index))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 37, "metadata": { "id": "fGbUe1WJYbxp" }, @@ -1169,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1204,10 +1189,13 @@ " exang\n", " cp_1.0\n", " cp_2.0\n", + " cp_3.0\n", " cp_4.0\n", + " restecg_1\n", " slope_1\n", " slope_2\n", " thal_3.0\n", + " thal_6.0\n", " thal_7.0\n", " \n", " \n", @@ -1218,10 +1206,13 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", @@ -1231,10 +1222,13 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 610\n", @@ -1242,11 +1236,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 328\n", @@ -1254,11 +1251,14 @@ " 0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 804\n", @@ -1266,11 +1266,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " ...\n", @@ -1283,6 +1286,9 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 374\n", @@ -1292,9 +1298,12 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 590\n", @@ -1302,11 +1311,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 573\n", @@ -1314,11 +1326,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 580\n", @@ -1326,11 +1341,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 308\n", @@ -1340,33 +1358,49 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", "\n", - "

230 rows × 9 columns

\n", + "

230 rows × 12 columns

\n", "" ], "text/plain": [ - " sex exang cp_1.0 cp_2.0 cp_4.0 slope_1 slope_2 thal_3.0 thal_7.0\n", - "272 1.0 1 0.0 0.0 1.0 0.0 1.0 0.0 1.0\n", - "59 1.0 1 1.0 0.0 0.0 1.0 0.0 1.0 0.0\n", - "610 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "328 1.0 0 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "804 1.0 1 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", - ".. ... ... ... ... ... ... ... ... ...\n", - "374 0.0 0 0.0 1.0 0.0 0.0 1.0 1.0 0.0\n", - "590 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "573 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "580 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "308 0.0 0 0.0 1.0 0.0 0.0 1.0 1.0 0.0\n", + " sex exang cp_1.0 cp_2.0 cp_3.0 cp_4.0 restecg_1 slope_1 slope_2 \\\n", + "272 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "59 1.0 1 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", + "610 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "328 1.0 0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "804 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "374 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "590 1.0 1 0.0 0.0 0.0 1.0 1.0 0.0 1.0 \n", + "573 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "580 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "308 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "\n", + " thal_3.0 thal_6.0 thal_7.0 \n", + "272 0.0 0.0 1.0 \n", + "59 1.0 0.0 0.0 \n", + "610 1.0 0.0 0.0 \n", + "328 1.0 0.0 0.0 \n", + "804 1.0 0.0 0.0 \n", + ".. ... ... ... \n", + "374 1.0 0.0 0.0 \n", + "590 1.0 0.0 0.0 \n", + "573 1.0 0.0 0.0 \n", + "580 1.0 0.0 0.0 \n", + "308 1.0 0.0 0.0 \n", "\n", - "[230 rows x 9 columns]" + "[230 rows x 12 columns]" ] }, - "execution_count": 12, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1377,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 39, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1401,7 +1435,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1429,10 +1463,13 @@ " exang\n", " cp_1.0\n", " cp_2.0\n", + " cp_3.0\n", " cp_4.0\n", + " restecg_1\n", " slope_1\n", " slope_2\n", " thal_3.0\n", + " thal_6.0\n", " thal_7.0\n", " \n", " \n", @@ -1443,10 +1480,13 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " \n", " \n", @@ -1456,10 +1496,13 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 610\n", @@ -1467,11 +1510,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 328\n", @@ -1479,11 +1525,14 @@ " 0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 804\n", @@ -1491,11 +1540,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " ...\n", @@ -1508,6 +1560,9 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 374\n", @@ -1517,9 +1572,12 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 590\n", @@ -1527,11 +1585,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 1.0\n", " 1.0\n", " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 573\n", @@ -1539,11 +1600,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 580\n", @@ -1551,11 +1615,14 @@ " 1\n", " 0.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", " 308\n", @@ -1565,33 +1632,49 @@ " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", + " 0.0\n", " 1.0\n", " 1.0\n", " 0.0\n", + " 0.0\n", " \n", " \n", "\n", - "

230 rows × 9 columns

\n", + "

230 rows × 12 columns

\n", "" ], "text/plain": [ - " sex exang cp_1.0 cp_2.0 cp_4.0 slope_1 slope_2 thal_3.0 thal_7.0\n", - "272 1.0 1 0.0 0.0 1.0 0.0 1.0 0.0 1.0\n", - "59 1.0 1 1.0 0.0 0.0 1.0 0.0 1.0 0.0\n", - "610 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "328 1.0 0 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "804 1.0 1 0.0 0.0 1.0 0.0 0.0 1.0 0.0\n", - ".. ... ... ... ... ... ... ... ... ...\n", - "374 0.0 0 0.0 1.0 0.0 0.0 1.0 1.0 0.0\n", - "590 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "573 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "580 1.0 1 0.0 0.0 1.0 0.0 1.0 1.0 0.0\n", - "308 0.0 0 0.0 1.0 0.0 0.0 1.0 1.0 0.0\n", + " sex exang cp_1.0 cp_2.0 cp_3.0 cp_4.0 restecg_1 slope_1 slope_2 \\\n", + "272 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "59 1.0 1 1.0 0.0 0.0 0.0 0.0 1.0 0.0 \n", + "610 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "328 1.0 0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "804 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "374 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "590 1.0 1 0.0 0.0 0.0 1.0 1.0 0.0 1.0 \n", + "573 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "580 1.0 1 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "308 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", "\n", - "[230 rows x 9 columns]" + " thal_3.0 thal_6.0 thal_7.0 \n", + "272 0.0 0.0 1.0 \n", + "59 1.0 0.0 0.0 \n", + "610 1.0 0.0 0.0 \n", + "328 1.0 0.0 0.0 \n", + "804 1.0 0.0 0.0 \n", + ".. ... ... ... \n", + "374 1.0 0.0 0.0 \n", + "590 1.0 0.0 0.0 \n", + "573 1.0 0.0 0.0 \n", + "580 1.0 0.0 0.0 \n", + "308 1.0 0.0 0.0 \n", + "\n", + "[230 rows x 12 columns]" ] }, - "execution_count": 14, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1602,26 +1685,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n", - " 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n", - " 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n", - " 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,\n", + " 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n", + " 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n", + " 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n", " 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n", " 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n", " 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n", " 0, 0, 1, 0, 1, 0, 1, 1, 1, 0], dtype=int64)" ] }, - "execution_count": 15, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1632,7 +1715,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1644,1951 +1727,2383 @@ "\n", "\n", - "\n", + "\n", "\n", "Tree\n", - "\n", + "\n", "\n", "\n", "0\n", - "\n", - "cp_4.0 <= 0.5\n", - "gini = 0.494\n", - "samples = 690\n", - "value = [308.0, 382.0]\n", + "\n", + "cp_4.0 <= 0.5\n", + "gini = 0.494\n", + "samples = 690\n", + "value = [308.0, 382.0]\n", "\n", "\n", "\n", "1\n", - "\n", - "sex <= 0.5\n", - "gini = 0.415\n", - "samples = 320\n", - "value = [226, 94]\n", + "\n", + "sex <= 0.5\n", + "gini = 0.415\n", + "samples = 320\n", + "value = [226, 94]\n", "\n", "\n", "\n", "0->1\n", - "\n", - "\n", - "True\n", + "\n", + "\n", + "True\n", "\n", - "\n", - "\n", - "86\n", - "\n", - "exang <= 0.5\n", - "gini = 0.345\n", - "samples = 370\n", - "value = [82, 288]\n", + "\n", + "\n", + "96\n", + "\n", + "exang <= 0.5\n", + "gini = 0.345\n", + "samples = 370\n", + "value = [82, 288]\n", "\n", - "\n", - "\n", - "0->86\n", - "\n", - "\n", - "False\n", + "\n", + "\n", + "0->96\n", + "\n", + "\n", + "False\n", "\n", "\n", "\n", "2\n", - "\n", - "thal_7.0 <= 0.5\n", - "gini = 0.172\n", - "samples = 95\n", - "value = [86, 9]\n", + "\n", + "thal_7.0 <= 0.5\n", + "gini = 0.172\n", + "samples = 95\n", + "value = [86, 9]\n", "\n", "\n", "\n", "1->2\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "29\n", - "\n", - "cp_2.0 <= 0.5\n", - "gini = 0.47\n", - "samples = 225\n", - "value = [140, 85]\n", + "\n", + "\n", + "31\n", + "\n", + "cp_2.0 <= 0.5\n", + "gini = 0.47\n", + "samples = 225\n", + "value = [140, 85]\n", "\n", - "\n", - "\n", - "1->29\n", - "\n", - "\n", + "\n", + "\n", + "1->31\n", + "\n", + "\n", "\n", "\n", "\n", "3\n", - "\n", - "cp_2.0 <= 0.5\n", - "gini = 0.126\n", - "samples = 89\n", - "value = [83, 6]\n", + "\n", + "cp_2.0 <= 0.5\n", + "gini = 0.126\n", + "samples = 89\n", + "value = [83, 6]\n", "\n", "\n", "\n", "2->3\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "22\n", - "\n", - "slope_1 <= 0.5\n", - "gini = 0.5\n", - "samples = 6\n", - "value = [3, 3]\n", + "\n", + "\n", + "24\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.5\n", + "samples = 6\n", + "value = [3, 3]\n", "\n", - "\n", - "\n", - "2->22\n", - "\n", - "\n", + "\n", + "\n", + "2->24\n", + "\n", + "\n", "\n", "\n", "\n", "4\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.162\n", - "samples = 45\n", - "value = [41, 4]\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.162\n", + "samples = 45\n", + "value = [41, 4]\n", "\n", "\n", "\n", "3->4\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "15\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.087\n", - "samples = 44\n", - "value = [42, 2]\n", + "\n", + "\n", + "13\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.087\n", + "samples = 44\n", + "value = [42, 2]\n", "\n", - "\n", - "\n", - "3->15\n", - "\n", - "\n", + "\n", + "\n", + "3->13\n", + "\n", + "\n", "\n", "\n", "\n", "5\n", - "\n", - "exang <= 0.5\n", - "gini = 0.087\n", - "samples = 22\n", - "value = [21, 1]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.087\n", + "samples = 22\n", + "value = [21, 1]\n", "\n", "\n", "\n", "4->5\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "10\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.227\n", - "samples = 23\n", - "value = [20, 3]\n", + "\n", + "\n", + "8\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.227\n", + "samples = 23\n", + "value = [20, 3]\n", "\n", - "\n", - "\n", - "4->10\n", - "\n", - "\n", + "\n", + "\n", + "4->8\n", + "\n", + "\n", "\n", "\n", "\n", "6\n", - "\n", - "cp_1.0 <= 0.5\n", - "gini = 0.1\n", - "samples = 19\n", - "value = [18, 1]\n", + "\n", + "gini = 0.0\n", + "samples = 21\n", + "value = [21, 0]\n", "\n", "\n", "\n", "5->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "9\n", - "\n", - "gini = 0.0\n", - "samples = 3\n", - "value = [3, 0]\n", - "\n", - "\n", - "\n", - "5->9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "7\n", - "\n", - "gini = 0.117\n", - "samples = 16\n", - "value = [15, 1]\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", "\n", - "\n", + "\n", "\n", - "6->7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "8\n", - "\n", - "gini = 0.0\n", - "samples = 3\n", - "value = [3, 0]\n", - "\n", - "\n", - "\n", - "6->8\n", - "\n", - "\n", + "5->7\n", + "\n", + "\n", "\n", - "\n", - "\n", - "11\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [1, 0]\n", + "\n", + "\n", + "9\n", + "\n", + "cp_1.0 <= 0.5\n", + "gini = 0.278\n", + "samples = 18\n", + "value = [15, 3]\n", "\n", - "\n", - "\n", - "10->11\n", - "\n", - "\n", + "\n", + "\n", + "8->9\n", + "\n", + "\n", "\n", "\n", "\n", "12\n", - "\n", - "cp_1.0 <= 0.5\n", - "gini = 0.236\n", - "samples = 22\n", - "value = [19, 3]\n", + "\n", + "gini = 0.0\n", + "samples = 5\n", + "value = [5, 0]\n", "\n", - "\n", + "\n", "\n", - "10->12\n", - "\n", - "\n", + "8->12\n", + "\n", + "\n", "\n", - "\n", - "\n", - "13\n", - "\n", - "gini = 0.208\n", - "samples = 17\n", - "value = [15, 2]\n", + "\n", + "\n", + "10\n", + "\n", + "gini = 0.245\n", + "samples = 14\n", + "value = [12, 2]\n", "\n", - "\n", - "\n", - "12->13\n", - "\n", - "\n", + "\n", + "\n", + "9->10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "11\n", + "\n", + "gini = 0.375\n", + "samples = 4\n", + "value = [3, 1]\n", + "\n", + "\n", + "\n", + "9->11\n", + "\n", + "\n", "\n", "\n", "\n", "14\n", - "\n", - "gini = 0.32\n", - "samples = 5\n", - "value = [4, 1]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.061\n", + "samples = 32\n", + "value = [31, 1]\n", "\n", - "\n", + "\n", "\n", - "12->14\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "16\n", - "\n", - "exang <= 0.5\n", - "gini = 0.153\n", - "samples = 12\n", - "value = [11, 1]\n", - "\n", - "\n", - "\n", - "15->16\n", - "\n", - "\n", + "13->14\n", + "\n", + "\n", "\n", "\n", "\n", "19\n", - "\n", - "exang <= 0.5\n", - "gini = 0.061\n", - "samples = 32\n", - "value = [31, 1]\n", + "\n", + "exang <= 0.5\n", + "gini = 0.153\n", + "samples = 12\n", + "value = [11, 1]\n", "\n", - "\n", + "\n", "\n", - "15->19\n", - "\n", - "\n", + "13->19\n", + "\n", + "\n", "\n", - "\n", - "\n", - "17\n", - "\n", - "gini = 0.18\n", - "samples = 10\n", - "value = [9, 1]\n", + "\n", + "\n", + "15\n", + "\n", + "exang <= 0.5\n", + "gini = 0.077\n", + "samples = 25\n", + "value = [24, 1]\n", "\n", - "\n", - "\n", - "16->17\n", - "\n", - "\n", + "\n", + "\n", + "14->15\n", + "\n", + "\n", "\n", "\n", "\n", "18\n", "\n", "gini = 0.0\n", - "samples = 2\n", - "value = [2, 0]\n", + "samples = 7\n", + "value = [7, 0]\n", "\n", - "\n", + "\n", "\n", - "16->18\n", - "\n", - "\n", + "14->18\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "16\n", + "\n", + "gini = 0.08\n", + "samples = 24\n", + "value = [23, 1]\n", + "\n", + "\n", + "\n", + "15->16\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "17\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", + "\n", + "\n", + "\n", + "15->17\n", + "\n", + "\n", "\n", "\n", "\n", "20\n", - "\n", - "gini = 0.062\n", - "samples = 31\n", - "value = [30, 1]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.18\n", + "samples = 10\n", + "value = [9, 1]\n", "\n", "\n", "\n", "19->20\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "21\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [1, 0]\n", - "\n", - "\n", - "\n", - "19->21\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "23\n", - "\n", - "cp_2.0 <= 0.5\n", - "gini = 0.48\n", - "samples = 5\n", - "value = [2, 3]\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [2, 0]\n", "\n", - "\n", + "\n", "\n", - "22->23\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "28\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [1, 0]\n", - "\n", - "\n", - "\n", - "22->28\n", - "\n", - "\n", + "19->23\n", + "\n", + "\n", "\n", - "\n", - "\n", - "24\n", - "\n", - "exang <= 0.5\n", - "gini = 0.444\n", - "samples = 3\n", - "value = [1, 2]\n", + "\n", + "\n", + "21\n", + "\n", + "gini = 0.198\n", + "samples = 9\n", + "value = [8, 1]\n", "\n", - "\n", - "\n", - "23->24\n", - "\n", - "\n", + "\n", + "\n", + "20->21\n", + "\n", + "\n", "\n", - "\n", - "\n", - "27\n", - "\n", - "gini = 0.5\n", - "samples = 2\n", - "value = [1, 1]\n", + "\n", + "\n", + "22\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", "\n", - "\n", - "\n", - "23->27\n", - "\n", - "\n", + "\n", + "\n", + "20->22\n", + "\n", + "\n", "\n", "\n", "\n", "25\n", - "\n", - "gini = 0.5\n", - "samples = 2\n", - "value = [1, 1]\n", + "\n", + "exang <= 0.5\n", + "gini = 0.48\n", + "samples = 5\n", + "value = [2, 3]\n", "\n", "\n", "\n", "24->25\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "26\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [0, 1]\n", - "\n", - "\n", - "\n", - "24->26\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "30\n", - "\n", - "slope_1 <= 0.5\n", - "gini = 0.5\n", - "samples = 137\n", - "value = [70, 67]\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", "\n", - "\n", + 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"\n", + "\n", + "27->28\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "29\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "\n", + "27->29\n", + "\n", + "\n", "\n", "\n", "\n", "32\n", - "\n", - "cp_1.0 <= 0.5\n", - "gini = 0.5\n", - "samples = 82\n", - "value = [41, 41]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.5\n", + "samples = 137\n", + "value = [70, 67]\n", "\n", "\n", "\n", "31->32\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "49\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.423\n", - "samples = 23\n", - "value = [7, 16]\n", + "\n", + "\n", + "73\n", + "\n", + "exang <= 0.5\n", + "gini = 0.325\n", + "samples = 88\n", + "value = [70, 18]\n", "\n", - "\n", - "\n", - "31->49\n", - "\n", - "\n", + "\n", + "\n", + "31->73\n", + "\n", + "\n", "\n", "\n", "\n", "33\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.498\n", - "samples = 64\n", - "value = [34, 30]\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.489\n", + "samples = 110\n", + "value = [63, 47]\n", "\n", "\n", "\n", "32->33\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "42\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.475\n", - "samples = 18\n", - "value = [7, 11]\n", + "\n", + "\n", + "62\n", + "\n", + "exang <= 0.5\n", + "gini = 0.384\n", + "samples = 27\n", + "value = [7, 20]\n", "\n", - "\n", - "\n", - "32->42\n", - "\n", - "\n", + "\n", + "\n", + "32->62\n", + "\n", + "\n", "\n", "\n", "\n", "34\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.444\n", - "samples = 12\n", - "value = [4, 8]\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.401\n", + "samples = 36\n", + "value = [26, 10]\n", "\n", "\n", "\n", "33->34\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "39\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.488\n", - "samples = 52\n", - "value = [30.0, 22.0]\n", + "\n", + "\n", + "49\n", + "\n", + "thal_6.0 <= 0.5\n", + "gini = 0.5\n", + "samples = 74\n", + "value = [37, 37]\n", "\n", - "\n", - "\n", - "33->39\n", - "\n", - "\n", + "\n", + "\n", + "33->49\n", + "\n", + "\n", "\n", "\n", "\n", "35\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [1, 0]\n", + "\n", + "cp_1.0 <= 0.5\n", + "gini = 0.198\n", + "samples = 9\n", + "value = [8, 1]\n", "\n", "\n", "\n", "34->35\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "42\n", + "\n", + "exang <= 0.5\n", + "gini = 0.444\n", + "samples = 27\n", + "value = [18, 9]\n", + "\n", + "\n", + "\n", + "34->42\n", + "\n", + "\n", "\n", "\n", "\n", "36\n", - "\n", - "thal_7.0 <= 0.5\n", - "gini = 0.397\n", - "samples = 11\n", - "value = [3, 8]\n", + "\n", + "exang <= 0.5\n", + "gini = 0.278\n", + "samples = 6\n", + "value = [5, 1]\n", "\n", - "\n", + "\n", "\n", - "34->36\n", - "\n", - "\n", + "35->36\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "41\n", + "\n", + "gini = 0.0\n", + "samples = 3\n", + "value = [3, 0]\n", + "\n", + "\n", + "\n", + "35->41\n", + "\n", + "\n", "\n", "\n", "\n", "37\n", - "\n", - "gini = 0.375\n", - "samples = 4\n", - "value = [1, 3]\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.32\n", + "samples = 5\n", + "value = [4, 1]\n", "\n", "\n", "\n", "36->37\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "38\n", - "\n", - "gini = 0.408\n", - "samples = 7\n", - "value = [2, 5]\n", - "\n", - "\n", - "\n", - "36->38\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "40\n", - "\n", - "gini = 0.5\n", - "samples = 4\n", - "value = [2, 2]\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", "\n", - "\n", + "\n", "\n", - "39->40\n", - "\n", - "\n", + "36->40\n", + "\n", + "\n", "\n", - "\n", - "\n", - "41\n", - "\n", - "gini = 0.486\n", - "samples = 48\n", - "value = [28, 20]\n", + "\n", + "\n", + "38\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", "\n", - "\n", - "\n", - "39->41\n", - "\n", - "\n", + "\n", + "\n", + "37->38\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "39\n", + "\n", + "gini = 0.375\n", + "samples = 4\n", + "value = [3, 1]\n", + "\n", + "\n", + "\n", + "37->39\n", + "\n", + "\n", "\n", "\n", "\n", "43\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.408\n", - "samples = 7\n", - "value = [5, 2]\n", + "\n", + "cp_1.0 <= 0.5\n", + "gini = 0.426\n", + "samples = 26\n", + "value = [18, 8]\n", "\n", "\n", "\n", "42->43\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "46\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.298\n", - "samples = 11\n", - "value = [2, 9]\n", + "\n", + "\n", + "48\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", "\n", - "\n", - "\n", - "42->46\n", - "\n", - "\n", + "\n", + "\n", + "42->48\n", + "\n", + "\n", "\n", "\n", "\n", "44\n", - "\n", - "gini = 0.0\n", - "samples = 2\n", - "value = [2, 0]\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.363\n", + "samples = 21\n", + "value = [16, 5]\n", "\n", "\n", "\n", "43->44\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "45\n", - "\n", - "gini = 0.48\n", - "samples = 5\n", - "value = [3, 2]\n", - "\n", - "\n", - "\n", - "43->45\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "47\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [0, 1]\n", + "\n", + "gini = 0.48\n", + "samples = 5\n", + "value = [2, 3]\n", "\n", - "\n", + "\n", "\n", - "46->47\n", - "\n", - "\n", + "43->47\n", + "\n", + "\n", "\n", - "\n", - "\n", - "48\n", - "\n", - "gini = 0.32\n", - "samples = 10\n", - "value = [2, 8]\n", + "\n", + "\n", + "45\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [2, 1]\n", "\n", - "\n", - "\n", - "46->48\n", - "\n", - "\n", + "\n", + "\n", + "44->45\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "46\n", + "\n", + "gini = 0.346\n", + "samples = 18\n", + "value = [14, 4]\n", + "\n", + "\n", + "\n", + "44->46\n", + "\n", + "\n", "\n", "\n", "\n", "50\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [0, 1]\n", + "\n", + "cp_3.0 <= 0.5\n", + "gini = 0.5\n", + "samples = 72\n", + "value = [37, 35]\n", "\n", "\n", "\n", "49->50\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "61\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [0, 2]\n", + "\n", + "\n", + "\n", + "49->61\n", + "\n", + "\n", "\n", "\n", "\n", "51\n", - "\n", - "cp_1.0 <= 0.5\n", - "gini = 0.434\n", - "samples = 22\n", - "value = [7.0, 15.0]\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.444\n", + "samples = 12\n", + "value = [4, 8]\n", "\n", - "\n", + "\n", "\n", - "49->51\n", - "\n", - "\n", + "50->51\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "54\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.495\n", + "samples = 60\n", + "value = [33, 27]\n", + "\n", + "\n", + "\n", + "50->54\n", + "\n", + "\n", "\n", "\n", "\n", "52\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.444\n", - "samples = 21\n", - "value = [7, 14]\n", + "\n", + "gini = 0.48\n", + "samples = 5\n", + "value = [3, 2]\n", "\n", "\n", "\n", "51->52\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "57\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [0, 1]\n", - "\n", - "\n", - "\n", - "51->57\n", - 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- "\n", - "105->109\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "107\n", - "\n", - "gini = 0.5\n", - "samples = 4\n", - "value = [2, 2]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.444\n", + "samples = 9\n", + "value = [3, 6]\n", "\n", "\n", "\n", "106->107\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "110\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", + "\n", + "\n", + "\n", + "106->110\n", + "\n", + "\n", "\n", "\n", "\n", "108\n", - "\n", - "gini = 0.474\n", - "samples = 70\n", - "value = [27, 43]\n", + "\n", + "gini = 0.469\n", + "samples = 8\n", + "value = [3, 5]\n", "\n", - "\n", + "\n", "\n", - "106->108\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "111\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.384\n", - "samples = 27\n", - "value = [7, 20]\n", - "\n", - "\n", - "\n", - "110->111\n", - "\n", - "\n", + "107->108\n", + "\n", + "\n", "\n", - "\n", - "\n", - "118\n", - "\n", - "thal_3.0 <= 0.5\n", - "gini = 0.169\n", - "samples = 183\n", - "value = [17, 166]\n", + "\n", + "\n", + "109\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", "\n", - "\n", - "\n", - "110->118\n", - "\n", - "\n", + "\n", + "\n", + "107->109\n", + "\n", + "\n", "\n", "\n", "\n", "112\n", - "\n", - "gini = 0.0\n", - "samples = 9\n", - "value = [0, 9]\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.397\n", + "samples = 22\n", + "value = [16, 6]\n", "\n", "\n", "\n", "111->112\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "119\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.473\n", + "samples = 94\n", + "value = [36, 58]\n", + "\n", + "\n", + "\n", + "111->119\n", + "\n", + "\n", "\n", "\n", "\n", "113\n", - "\n", - "slope_1 <= 0.5\n", - "gini = 0.475\n", - "samples = 18\n", - "value = [7, 11]\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.48\n", + "samples = 10\n", + "value = [6, 4]\n", "\n", - "\n", + "\n", "\n", - "111->113\n", - "\n", - "\n", + "112->113\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "116\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.278\n", + "samples = 12\n", + "value = [10, 2]\n", + "\n", + "\n", + "\n", + "112->116\n", + "\n", + "\n", "\n", "\n", "\n", "114\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.444\n", - "samples = 15\n", - "value = [5, 10]\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [0, 2]\n", "\n", "\n", "\n", "113->114\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "117\n", - "\n", - "gini = 0.444\n", - "samples = 3\n", - "value = [2, 1]\n", - "\n", - "\n", - "\n", - "113->117\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "115\n", - "\n", - "gini = 0.0\n", - "samples = 1\n", - "value = [0, 1]\n", + "\n", + "gini = 0.375\n", + "samples = 8\n", + "value = [6, 2]\n", "\n", - "\n", + "\n", "\n", - "114->115\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "116\n", - "\n", - "gini = 0.459\n", - "samples = 14\n", - "value = [5, 9]\n", - "\n", - "\n", - "\n", - "114->116\n", - "\n", - "\n", + "113->115\n", + "\n", + "\n", "\n", - "\n", - "\n", - "119\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.123\n", - "samples = 76\n", - "value = [5, 71]\n", + "\n", + "\n", + "117\n", + "\n", + "gini = 0.198\n", + "samples = 9\n", + "value = [8, 1]\n", "\n", - "\n", - "\n", - "118->119\n", - "\n", - "\n", + "\n", + "\n", + "116->117\n", + "\n", + "\n", "\n", - "\n", - "\n", - "128\n", - "\n", - "slope_2 <= 0.5\n", - "gini = 0.199\n", - "samples = 107\n", - "value = [12, 95]\n", + "\n", + "\n", + "118\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [2, 1]\n", "\n", - "\n", - "\n", - "118->128\n", - "\n", - "\n", + "\n", + "\n", + "116->118\n", + "\n", + "\n", "\n", "\n", "\n", "120\n", - "\n", - "thal_7.0 <= 0.5\n", - "gini = 0.185\n", - "samples = 29\n", - "value = [3, 26]\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.477\n", + "samples = 74\n", + "value = [29, 45]\n", "\n", "\n", "\n", "119->120\n", - "\n", - "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "125\n", - "\n", - "thal_7.0 <= 0.5\n", - "gini = 0.081\n", - "samples = 47\n", - "value = [2, 45]\n", + "\n", + "\n", + "127\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.455\n", + "samples = 20\n", + "value = [7, 13]\n", "\n", - "\n", - "\n", - "119->125\n", - "\n", - "\n", + "\n", + "\n", + "119->127\n", + "\n", + "\n", "\n", "\n", "\n", "121\n", - "\n", - "gini = 0.0\n", - "samples = 2\n", - "value = [0, 2]\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.46\n", + "samples = 53\n", + "value = [19, 34]\n", "\n", "\n", "\n", "120->121\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "124\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.499\n", + "samples = 21\n", + "value = [10, 11]\n", + "\n", + "\n", + "\n", + "120->124\n", + "\n", + "\n", "\n", "\n", "\n", "122\n", - "\n", - "slope_1 <= 0.5\n", - "gini = 0.198\n", - "samples = 27\n", - "value = [3, 24]\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [1, 2]\n", "\n", - "\n", + "\n", "\n", - "120->122\n", - "\n", - "\n", + "121->122\n", + "\n", + "\n", "\n", "\n", "\n", "123\n", - "\n", - "gini = 0.198\n", - "samples = 9\n", - "value = [1, 8]\n", + "\n", + "gini = 0.461\n", + "samples = 50\n", + "value = [18, 32]\n", "\n", - "\n", + "\n", "\n", - "122->123\n", - "\n", - "\n", + "121->123\n", + "\n", + "\n", "\n", - "\n", - "\n", - "124\n", - "\n", - "gini = 0.198\n", - "samples = 18\n", - "value = [2, 16]\n", + "\n", + "\n", + "125\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [1, 0]\n", "\n", - "\n", - "\n", - "122->124\n", - "\n", - "\n", + "\n", + "\n", + "124->125\n", + "\n", + "\n", "\n", "\n", "\n", "126\n", - "\n", - "gini = 0.0\n", - "samples = 9\n", - "value = [0, 9]\n", + "\n", + "gini = 0.495\n", + "samples = 20\n", + "value = [9, 11]\n", "\n", - "\n", + "\n", "\n", - "125->126\n", - "\n", - "\n", + "124->126\n", + "\n", + "\n", "\n", - "\n", - "\n", - "127\n", - "\n", - "gini = 0.1\n", - "samples = 38\n", - "value = [2, 36]\n", + "\n", + "\n", + "128\n", + "\n", + "gini = 0.492\n", + "samples = 16\n", + "value = [7, 9]\n", "\n", - "\n", - "\n", - "125->127\n", - "\n", - "\n", + "\n", + "\n", + "127->128\n", + "\n", + "\n", "\n", "\n", "\n", "129\n", - "\n", - "slope_1 <= 0.5\n", - "gini = 0.142\n", - "samples = 26\n", - "value = [2, 24]\n", + "\n", + "gini = 0.0\n", + "samples = 4\n", + "value = [0, 4]\n", "\n", - "\n", + "\n", "\n", - "128->129\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "132\n", - "\n", - "gini = 0.216\n", - "samples = 81\n", - "value = [10, 71]\n", - "\n", - "\n", - "\n", - "128->132\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "130\n", - "\n", - "gini = 0.124\n", - "samples = 15\n", - "value = [1, 14]\n", - "\n", - "\n", - "\n", - "129->130\n", - "\n", - "\n", + "127->129\n", + "\n", + "\n", "\n", "\n", "\n", "131\n", - "\n", - "gini = 0.165\n", - "samples = 11\n", - "value = [1, 10]\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.384\n", + "samples = 27\n", + "value = [7, 20]\n", "\n", - "\n", + "\n", "\n", - "129->131\n", - "\n", - "\n", + "130->131\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "140\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.169\n", + "samples = 183\n", + "value = [17, 166]\n", + "\n", + "\n", + "\n", + "130->140\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "132\n", + "\n", + "gini = 0.0\n", + "samples = 9\n", + "value = [0, 9]\n", + "\n", + "\n", + "\n", + "131->132\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "133\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.475\n", + "samples = 18\n", + "value = [7, 11]\n", + "\n", + "\n", + "\n", + "131->133\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "134\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.444\n", + "samples = 15\n", + "value = [5, 10]\n", + "\n", + "\n", + "\n", + "133->134\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "139\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [2, 1]\n", + "\n", + "\n", + "\n", + "133->139\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "135\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "\n", + "134->135\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "136\n", + "\n", + "restecg_1 <= 0.5\n", + "gini = 0.459\n", + "samples = 14\n", + "value = [5, 9]\n", + "\n", + "\n", + "\n", + "134->136\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "137\n", + "\n", + "gini = 0.463\n", + "samples = 11\n", + "value = [4, 7]\n", + "\n", + "\n", + "\n", + "136->137\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "138\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [1, 2]\n", + "\n", + "\n", + "\n", + "136->138\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "141\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.146\n", + "samples = 139\n", + "value = [11, 128]\n", + "\n", + "\n", + "\n", + "140->141\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "156\n", + "\n", + "slope_2 <= 0.5\n", + "gini = 0.236\n", + "samples = 44\n", + "value = [6, 38]\n", + "\n", + "\n", + "\n", + "140->156\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "142\n", + "\n", + "slope_1 <= 0.5\n", + "gini = 0.206\n", + "samples = 43\n", + "value = [5, 38]\n", + "\n", + "\n", + "\n", + "141->142\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "151\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.117\n", + "samples = 96\n", + "value = [6, 90]\n", + "\n", + "\n", + "\n", + "141->151\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "143\n", + "\n", + "thal_7.0 <= 0.5\n", + "gini = 0.172\n", + "samples = 21\n", + "value = [2, 19]\n", + "\n", + "\n", + "\n", + "142->143\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "148\n", + "\n", + "thal_3.0 <= 0.5\n", + "gini = 0.236\n", + "samples = 22\n", + "value = [3, 19]\n", + "\n", + "\n", + "\n", + "142->148\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "144\n", + "\n", + "thal_6.0 <= 0.5\n", + "gini = 0.142\n", + "samples = 13\n", + "value = [1, 12]\n", + "\n", + "\n", + "\n", + "143->144\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "147\n", + "\n", + "gini = 0.219\n", + "samples = 8\n", + "value = [1, 7]\n", + "\n", + "\n", + "\n", + "143->147\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "145\n", + "\n", + "gini = 0.153\n", + "samples = 12\n", + "value = [1, 11]\n", + "\n", + "\n", + "\n", + "144->145\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "146\n", + "\n", + "gini = 0.0\n", + "samples = 1\n", + "value = [0, 1]\n", + "\n", + "\n", + "\n", + "144->146\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "149\n", + "\n", + "gini = 0.231\n", + "samples = 15\n", + "value = [2, 13]\n", + "\n", + "\n", + "\n", + "148->149\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "150\n", + "\n", + "gini = 0.245\n", + "samples = 7\n", + "value = [1, 6]\n", + "\n", + "\n", + "\n", + "148->150\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "152\n", + "\n", + "thal_6.0 <= 0.5\n", + "gini = 0.046\n", + "samples = 42\n", + "value = [1, 41]\n", + "\n", + "\n", + "\n", + "151->152\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "155\n", + "\n", + "gini = 0.168\n", + "samples = 54\n", + "value = [5, 49]\n", + "\n", + "\n", + "\n", + "151->155\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "153\n", + "\n", + "gini = 0.056\n", + "samples = 35\n", + "value = [1, 34]\n", + "\n", + "\n", + "\n", + "152->153\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "154\n", + "\n", + "gini = 0.0\n", + "samples = 7\n", + "value = [0, 7]\n", + "\n", + "\n", + "\n", + "152->154\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "157\n", + "\n", + "gini = 0.0\n", + "samples = 12\n", + "value = [0, 12]\n", + "\n", + "\n", + "\n", + "156->157\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "158\n", + "\n", + "thal_6.0 <= 0.5\n", + "gini = 0.305\n", + "samples = 32\n", + "value = [6, 26]\n", + "\n", + "\n", + "\n", + "156->158\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "159\n", + "\n", + "thal_7.0 <= 0.5\n", + "gini = 0.32\n", + "samples = 30\n", + "value = [6, 24]\n", + "\n", + "\n", + "\n", + "158->159\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "162\n", + "\n", + "gini = 0.0\n", + "samples = 2\n", + "value = [0, 2]\n", + "\n", + "\n", + "\n", + "158->162\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "160\n", + "\n", + "gini = 0.302\n", + "samples = 27\n", + "value = [5, 22]\n", + "\n", + "\n", + "\n", + "159->160\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "161\n", + "\n", + "gini = 0.444\n", + "samples = 3\n", + "value = [1, 2]\n", + "\n", + "\n", + "\n", + "159->161\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import graphviz\n", - "from sklearn import tree\n", - "\n", "tree.export_graphviz(clf, feature_names=clf.feature_names_in_, rounded=True, out_file='decision.dot')\n", "\n", "graphviz.Source(open('./decision.dot').read())"