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hyperparamtuningtest.ipynb
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"name": "HyperParamTuningTest.ipynb",
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"<a href=\"https://colab.research.google.com/gist/jimbonant/793a79b7cdc8378eb7ac6034c1f9bbaf/hyperparamtuningtest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "HnGn68STZZGo"
},
"outputs": [],
"source": [
"from sklearn import svm, datasets\n",
"import pandas as pd\n"
]
},
{
"cell_type": "code",
"source": [
"data = datasets.load_iris()"
],
"metadata": {
"id": "MYrCT7faZmTb"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame(data.data, columns=data.feature_names)"
],
"metadata": {
"id": "ew1GJjQTG1oY"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"df['flower'] = data.target\n",
"df['flower'] = df['flower'].apply(lambda x:data.target_names[x])"
],
"metadata": {
"id": "9SKfSATvHrla"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df[47:52]"
],
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"height": 206
},
"id": "Dj8WA8A3CxEr",
"outputId": "e1661fb4-3570-4110-aec2-5677c9ebde33"
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"outputs": [
{
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"data": {
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
"47 4.6 3.2 1.4 0.2 \n",
"48 5.3 3.7 1.5 0.2 \n",
"49 5.0 3.3 1.4 0.2 \n",
"50 7.0 3.2 4.7 1.4 \n",
"51 6.4 3.2 4.5 1.5 \n",
"\n",
" flower \n",
"47 setosa \n",
"48 setosa \n",
"49 setosa \n",
"50 versicolor \n",
"51 versicolor "
],
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" <th></th>\n",
" <th>sepal length (cm)</th>\n",
" <th>sepal width (cm)</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" <th>flower</th>\n",
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},
"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, Y_train, Y_test = train_test_split(data.data, data.target, test_size=.3)"
],
"metadata": {
"id": "Qj1DzecmLTCT"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
""
],
"metadata": {
"id": "dnjOkj_qM3PI"
}
},
{
"cell_type": "code",
"source": [
"model = svm.SVC(kernel='rbf', C=30, gamma='auto')\n",
"model.fit(X_train,Y_train)\n",
"model.score(X_test, Y_test)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XALE2PVTM5eI",
"outputId": "fb9b32e9-b45e-4892-d6a7-568a536e9f3c"
},
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9333333333333333"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import cross_val_score\n",
"\n",
"cross_val_score(svm.SVC(kernel='linear', C=10,gamma='auto'), data.data, data.target,cv=5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d2khA1L0Tjj1",
"outputId": "ac3500bd-6da5-4798-b301-791b3846a146"
},
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1. , 1. , 0.9 , 0.96666667, 1. ])"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "IkMt-3V0W3n0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cross_val_score(svm.SVC(kernel='rbf', C=10,gamma='auto'), data.data, data.target,cv=5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7b196110-2a02-4257-ba14-2841d14f95a1",
"id": "f7sjLEijW36w"
},
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.96666667, 1. , 0.96666667, 0.96666667, 1. ])"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"cross_val_score(svm.SVC(kernel='rbf', C=20,gamma='auto'), data.data, data.target,cv=5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HGpvUINiXv-L",
"outputId": "f644bbac-ff00-4bc8-a770-2684571bd51b"
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.96666667, 1. , 0.9 , 0.96666667, 1. ])"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import GridSearchCV"
],
"metadata": {
"id": "YpVduFXnYy6S"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"clf = GridSearchCV(svm.SVC(gamma='auto'), {\n",
" 'C':[1,10,20],\n",
" 'kernel' : ['rbf', 'linear']\n",
"}, cv=5, return_train_score=False)\n",
"clf.fit(data.data,data.target) "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z0f5yKqJY5MP",
"outputId": "035d46a8-fc45-46d3-cf42-c6513c7ddad2"
},
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=5, estimator=SVC(gamma='auto'),\n",
" param_grid={'C': [1, 10, 20], 'kernel': ['rbf', 'linear']})"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"df2 = pd.DataFrame(clf.cv_results_)\n",
"df2"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 452
},
"id": "FyCoT6jLZjqt",
"outputId": "099a58aa-3d65-406e-c68a-1943be3f2476"
},
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n",
"0 0.002222 0.001512 0.000770 0.000059 1 \n",
"1 0.001299 0.000448 0.000568 0.000027 1 \n",
"2 0.001315 0.000028 0.000669 0.000025 10 \n",
"3 0.001061 0.000052 0.000588 0.000056 10 \n",
"4 0.001287 0.000082 0.000697 0.000070 20 \n",
"5 0.001086 0.000136 0.000604 0.000020 20 \n",
"\n",
" param_kernel params split0_test_score \\\n",
"0 rbf {'C': 1, 'kernel': 'rbf'} 0.966667 \n",
"1 linear {'C': 1, 'kernel': 'linear'} 0.966667 \n",
"2 rbf {'C': 10, 'kernel': 'rbf'} 0.966667 \n",
"3 linear {'C': 10, 'kernel': 'linear'} 1.000000 \n",
"4 rbf {'C': 20, 'kernel': 'rbf'} 0.966667 \n",
"5 linear {'C': 20, 'kernel': 'linear'} 1.000000 \n",
"\n",
" split1_test_score split2_test_score split3_test_score split4_test_score \\\n",
"0 1.0 0.966667 0.966667 1.0 \n",
"1 1.0 0.966667 0.966667 1.0 \n",
"2 1.0 0.966667 0.966667 1.0 \n",
"3 1.0 0.900000 0.966667 1.0 \n",
"4 1.0 0.900000 0.966667 1.0 \n",
"5 1.0 0.900000 0.933333 1.0 \n",
"\n",
" mean_test_score std_test_score rank_test_score \n",
"0 0.980000 0.016330 1 \n",
"1 0.980000 0.016330 1 \n",
"2 0.980000 0.016330 1 \n",
"3 0.973333 0.038873 4 \n",
"4 0.966667 0.036515 5 \n",
"5 0.966667 0.042164 6 "
],
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" <th></th>\n",
" <th>mean_fit_time</th>\n",
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" <th>0</th>\n",
" <td>0.002222</td>\n",
" <td>0.001512</td>\n",
" <td>0.000770</td>\n",
" <td>0.000059</td>\n",
" <td>1</td>\n",
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" <th>1</th>\n",
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" <td>0.000027</td>\n",
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" <td>{'C': 1, 'kernel': 'linear'}</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>0.001315</td>\n",
" <td>0.000028</td>\n",
" <td>0.000669</td>\n",
" <td>0.000025</td>\n",
" <td>10</td>\n",
" <td>rbf</td>\n",
" <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
" <td>0.966667</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
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" <td>0.016330</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>0.001061</td>\n",
" <td>0.000052</td>\n",
" <td>0.000588</td>\n",
" <td>0.000056</td>\n",
" <td>10</td>\n",
" <td>linear</td>\n",
" <td>{'C': 10, 'kernel': 'linear'}</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>0.900000</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
" <td>0.973333</td>\n",
" <td>0.038873</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.001287</td>\n",
" <td>0.000082</td>\n",
" <td>0.000697</td>\n",
" <td>0.000070</td>\n",
" <td>20</td>\n",
" <td>rbf</td>\n",
" <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
" <td>0.900000</td>\n",
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" <td>1.0</td>\n",
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" <td>5</td>\n",
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" <th>5</th>\n",
" <td>0.001086</td>\n",
" <td>0.000136</td>\n",
" <td>0.000604</td>\n",
" <td>0.000020</td>\n",
" <td>20</td>\n",
" <td>linear</td>\n",
" <td>{'C': 20, 'kernel': 'linear'}</td>\n",
" <td>1.000000</td>\n",
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" <td>0.933333</td>\n",
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" <td>6</td>\n",
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" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
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"\n",
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" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
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" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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" background-color: #434B5C;\n",
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" document.querySelector('#df-2f765e23-d6b9-487a-ba3f-ac0aa18ab77f button.colab-df-convert');\n",
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"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-2f765e23-d6b9-487a-ba3f-ac0aa18ab77f');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import RandomizedSearchCV\n",
"clf = RandomizedSearchCV(svm.SVC(gamma='auto'), {\n",
" 'C':[1,10,20],\n",
" 'kernel' : ['rbf', 'linear']\n",
"}, cv=5, return_train_score=False, n_iter=2)\n",
"clf.fit(data.data,data.target) "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vBTExP6shl2L",
"outputId": "a2affed8-d66f-44ca-f44e-8b683066e68a"
},
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomizedSearchCV(cv=5, estimator=SVC(gamma='auto'), n_iter=2,\n",
" param_distributions={'C': [1, 10, 20],\n",
" 'kernel': ['rbf', 'linear']})"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame(clf.cv_results_)\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 246
},
"id": "BCC66zu2e1WB",
"outputId": "1c492ac0-ce9f-47cc-a6ea-f9716891966f"
},
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean_fit_time std_fit_time mean_score_time std_score_time param_kernel \\\n",
"0 0.000822 0.000246 0.000514 0.000296 linear \n",
"1 0.000921 0.000197 0.000755 0.000633 rbf \n",
"\n",
" param_C params split0_test_score \\\n",
"0 20 {'kernel': 'linear', 'C': 20} 1.000000 \n",
"1 20 {'kernel': 'rbf', 'C': 20} 0.966667 \n",
"\n",
" split1_test_score split2_test_score split3_test_score split4_test_score \\\n",
"0 1.0 0.9 0.933333 1.0 \n",
"1 1.0 0.9 0.966667 1.0 \n",
"\n",
" mean_test_score std_test_score rank_test_score \n",
"0 0.966667 0.042164 2 \n",
"1 0.966667 0.036515 1 "
],
"text/html": [
"\n",
" <div id=\"df-ff0284ea-c51d-4229-9b49-346d9200f128\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_fit_time</th>\n",
" <th>std_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>std_score_time</th>\n",
" <th>param_kernel</th>\n",
" <th>param_C</th>\n",
" <th>params</th>\n",
" <th>split0_test_score</th>\n",
" <th>split1_test_score</th>\n",
" <th>split2_test_score</th>\n",
" <th>split3_test_score</th>\n",
" <th>split4_test_score</th>\n",
" <th>mean_test_score</th>\n",
" <th>std_test_score</th>\n",
" <th>rank_test_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000822</td>\n",
" <td>0.000246</td>\n",
" <td>0.000514</td>\n",
" <td>0.000296</td>\n",
" <td>linear</td>\n",
" <td>20</td>\n",
" <td>{'kernel': 'linear', 'C': 20}</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>0.933333</td>\n",
" <td>1.0</td>\n",
" <td>0.966667</td>\n",
" <td>0.042164</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000921</td>\n",
" <td>0.000197</td>\n",
" <td>0.000755</td>\n",
" <td>0.000633</td>\n",
" <td>rbf</td>\n",
" <td>20</td>\n",
" <td>{'kernel': 'rbf', 'C': 20}</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
" <td>0.9</td>\n",
" <td>0.966667</td>\n",
" <td>1.0</td>\n",
" <td>0.966667</td>\n",
" <td>0.036515</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff0284ea-c51d-4229-9b49-346d9200f128')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
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" }\n",
"\n",
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"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-ff0284ea-c51d-4229-9b49-346d9200f128 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-ff0284ea-c51d-4229-9b49-346d9200f128');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.linear_model import LogisticRegression"
],
"metadata": {
"id": "WMGkDsybmjnv"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model_params = {\n",
" 'svm': {\n",
" 'model' : svm.SVC(gamma='auto'),\n",
" 'params' : {\n",
" 'C' : [1,10,20],\n",
" 'kernel' : ['rbf', 'linear']\n",
" }\n",
" },\n",
" 'random-forest': {\n",
" 'model' : RandomForestClassifier(),\n",
" 'params' : {\n",
" 'n_estimators': [1,5,10]\n",
" }\n",
" },\n",
" 'logistic_regression' : {\n",
" 'model': LogisticRegression(solver='liblinear', multi_class='auto'),\n",
" 'params' : {\n",
" 'C' : [1,5,10]\n",
" }\n",
" }\n",
"}"
],
"metadata": {
"id": "3fFrygclTqvW"
},
"execution_count": 60,
"outputs": []
},
{
"cell_type": "code",
"source": [
"scores = []\n",
"\n",
"for model_name, mp in model_params.items():\n",
" clf = GridSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n",
" clf.fit(data.data, data.target)\n",
" scores.append(\n",
" {\n",
" 'model': model_name,\n",
" 'best_score': clf.best_score_,\n",
" 'best_params' : clf.best_params_\n",
" }\n",
" )"
],
"metadata": {
"id": "HVKUNk3woa1d"
},
"execution_count": 61,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame(scores, columns = ['model','best_score','best_params'])\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 143
},
"id": "HE0fFscTp3Yx",
"outputId": "de41b93b-8498-45c2-f79b-fcaa1d662d3d"
},
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" model best_score best_params\n",
"0 svm 0.980000 {'C': 1, 'kernel': 'rbf'}\n",
"1 random-forest 0.966667 {'n_estimators': 10}\n",
"2 logistic_regression 0.966667 {'C': 5}"
],
"text/html": [
"\n",
" <div id=\"df-a5122d2b-2c34-43c4-8d99-62e192dd2e1e\">\n",
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" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>model</th>\n",
" <th>best_score</th>\n",
" <th>best_params</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>svm</td>\n",
" <td>0.980000</td>\n",
" <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>random-forest</td>\n",
" <td>0.966667</td>\n",
" <td>{'n_estimators': 10}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>logistic_regression</td>\n",
" <td>0.966667</td>\n",
" <td>{'C': 5}</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5122d2b-2c34-43c4-8d99-62e192dd2e1e')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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" </svg>\n",
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" cursor: pointer;\n",
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" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-a5122d2b-2c34-43c4-8d99-62e192dd2e1e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-a5122d2b-2c34-43c4-8d99-62e192dd2e1e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
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" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 62
}
]
}
]
}
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