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"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/github/Orrm23/DeepSeek-Coder/blob/main/LeafSpeciesDetection_DECISIONTREE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aYYh8N1KOChK"
},
"source": [
"# **Day-5 | Leaf Species Detection | DECISION TREE**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TUcGB8gXNufD"
},
"source": [
"### *Import basic Libraries*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XBeYR9xuNn_1"
},
"source": [
"from sklearn.datasets import load_iris\n",
"import pandas as pd\n",
"import numpy as np"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RSWsaAz7OOzj"
},
"source": [
"### *Load Dataset*"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1gYg7y7WOai_"
},
"source": [
"dataset = load_iris()"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ql3v6_gZOjAD"
},
"source": [
"### *Summarize Dataset*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_x9QzbrMOl0l",
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"outputId": "ab81e859-9cc0-4483-c174-5012b2b47ca4"
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},
"source": [
"print(dataset.data)\n",
"print(dataset.target)\n",
"\n",
"print(dataset.data.shape)"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[5.1 3.5 1.4 0.2]\n",
" [4.9 3. 1.4 0.2]\n",
" [4.7 3.2 1.3 0.2]\n",
" [4.6 3.1 1.5 0.2]\n",
" [5. 3.6 1.4 0.2]\n",
" [5.4 3.9 1.7 0.4]\n",
" [4.6 3.4 1.4 0.3]\n",
" [5. 3.4 1.5 0.2]\n",
" [4.4 2.9 1.4 0.2]\n",
" [4.9 3.1 1.5 0.1]\n",
" [5.4 3.7 1.5 0.2]\n",
" [4.8 3.4 1.6 0.2]\n",
" [4.8 3. 1.4 0.1]\n",
" [4.3 3. 1.1 0.1]\n",
" [5.8 4. 1.2 0.2]\n",
" [5.7 4.4 1.5 0.4]\n",
" [5.4 3.9 1.3 0.4]\n",
" [5.1 3.5 1.4 0.3]\n",
" [5.7 3.8 1.7 0.3]\n",
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" [4.4 3. 1.3 0.2]\n",
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" [5. 3.5 1.3 0.3]\n",
" [4.5 2.3 1.3 0.3]\n",
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" [5.1 3.8 1.6 0.2]\n",
" [4.6 3.2 1.4 0.2]\n",
" [5.3 3.7 1.5 0.2]\n",
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" [5.5 2.3 4. 1.3]\n",
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" [5.7 2.8 4.5 1.3]\n",
" [6.3 3.3 4.7 1.6]\n",
" [4.9 2.4 3.3 1. ]\n",
" [6.6 2.9 4.6 1.3]\n",
" [5.2 2.7 3.9 1.4]\n",
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" [5.9 3. 4.2 1.5]\n",
" [6. 2.2 4. 1. ]\n",
" [6.1 2.9 4.7 1.4]\n",
" [5.6 2.9 3.6 1.3]\n",
" [6.7 3.1 4.4 1.4]\n",
" [5.6 3. 4.5 1.5]\n",
" [5.8 2.7 4.1 1. ]\n",
" [6.2 2.2 4.5 1.5]\n",
" [5.6 2.5 3.9 1.1]\n",
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" [6.1 2.8 4.7 1.2]\n",
" [6.4 2.9 4.3 1.3]\n",
" [6.6 3. 4.4 1.4]\n",
" [6.8 2.8 4.8 1.4]\n",
" [6.7 3. 5. 1.7]\n",
" [6. 2.9 4.5 1.5]\n",
" [5.7 2.6 3.5 1. ]\n",
" [5.5 2.4 3.8 1.1]\n",
" [5.5 2.4 3.7 1. ]\n",
" [5.8 2.7 3.9 1.2]\n",
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" [6.7 3.1 4.7 1.5]\n",
" [6.3 2.3 4.4 1.3]\n",
" [5.6 3. 4.1 1.3]\n",
" [5.5 2.5 4. 1.3]\n",
" [5.5 2.6 4.4 1.2]\n",
" [6.1 3. 4.6 1.4]\n",
" [5.8 2.6 4. 1.2]\n",
" [5. 2.3 3.3 1. ]\n",
" [5.6 2.7 4.2 1.3]\n",
" [5.7 3. 4.2 1.2]\n",
" [5.7 2.9 4.2 1.3]\n",
" [6.2 2.9 4.3 1.3]\n",
" [5.1 2.5 3. 1.1]\n",
" [5.7 2.8 4.1 1.3]\n",
" [6.3 3.3 6. 2.5]\n",
" [5.8 2.7 5.1 1.9]\n",
" [7.1 3. 5.9 2.1]\n",
" [6.3 2.9 5.6 1.8]\n",
" [6.5 3. 5.8 2.2]\n",
" [7.6 3. 6.6 2.1]\n",
" [4.9 2.5 4.5 1.7]\n",
" [7.3 2.9 6.3 1.8]\n",
" [6.7 2.5 5.8 1.8]\n",
" [7.2 3.6 6.1 2.5]\n",
" [6.5 3.2 5.1 2. ]\n",
" [6.4 2.7 5.3 1.9]\n",
" [6.8 3. 5.5 2.1]\n",
" [5.7 2.5 5. 2. ]\n",
" [5.8 2.8 5.1 2.4]\n",
" [6.4 3.2 5.3 2.3]\n",
" [6.5 3. 5.5 1.8]\n",
" [7.7 3.8 6.7 2.2]\n",
" [7.7 2.6 6.9 2.3]\n",
" [6. 2.2 5. 1.5]\n",
" [6.9 3.2 5.7 2.3]\n",
" [5.6 2.8 4.9 2. ]\n",
" [7.7 2.8 6.7 2. ]\n",
" [6.3 2.7 4.9 1.8]\n",
" [6.7 3.3 5.7 2.1]\n",
" [7.2 3.2 6. 1.8]\n",
" [6.2 2.8 4.8 1.8]\n",
" [6.1 3. 4.9 1.8]\n",
" [6.4 2.8 5.6 2.1]\n",
" [7.2 3. 5.8 1.6]\n",
" [7.4 2.8 6.1 1.9]\n",
" [7.9 3.8 6.4 2. ]\n",
" [6.4 2.8 5.6 2.2]\n",
" [6.3 2.8 5.1 1.5]\n",
" [6.1 2.6 5.6 1.4]\n",
" [7.7 3. 6.1 2.3]\n",
" [6.3 3.4 5.6 2.4]\n",
" [6.4 3.1 5.5 1.8]\n",
" [6. 3. 4.8 1.8]\n",
" [6.9 3.1 5.4 2.1]\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2]\n",
"(150, 4)\n"
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},
{
"cell_type": "markdown",
"metadata": {
"id": "TZJDaTW3Or3X"
},
"source": [
"### *Segregate Dataset into X(Input/IndependentVariable) & Y(Output/DependentVariable)*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
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"id": "EG1_3L0hOoq0",
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"outputId": "4497898b-6264-4286-abb3-6394ec005754"
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"source": [
"X = pd.DataFrame(dataset.data, columns=dataset.feature_names)\n",
"X"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2\n",
".. ... ... ... ...\n",
"145 6.7 3.0 5.2 2.3\n",
"146 6.3 2.5 5.0 1.9\n",
"147 6.5 3.0 5.2 2.0\n",
"148 6.2 3.4 5.4 2.3\n",
"149 5.9 3.0 5.1 1.8\n",
"\n",
"[150 rows x 4 columns]"
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" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
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" document.querySelector('#df-3fa5a8c8-0d89-4e94-9eb6-a6f83fb4b61a button');\n",
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" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
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" <div id=\"id_cd5c8bd3-0029-41c9-854f-eed3f438d4f0\">\n",
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" <style>\n",
" .colab-df-generate {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-generate: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-generate {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-generate: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",
" <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X')\"\n",
" title=\"Generate code using this dataframe.\"\n",
" style=\"display:none;\">\n",
"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
" </svg>\n",
" </button>\n",
" <script>\n",
" (() => {\n",
" const buttonEl =\n",
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" document.querySelector('#id_cd5c8bd3-0029-41c9-854f-eed3f438d4f0 button.colab-df-generate');\n",
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" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" buttonEl.onclick = () => {\n",
" google.colab.notebook.generateWithVariable('X');\n",
" }\n",
" })();\n",
" </script>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "X",
"summary": "{\n \"name\": \"X\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"sepal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8280661279778629,\n \"min\": 4.3,\n \"max\": 7.9,\n \"num_unique_values\": 35,\n \"samples\": [\n 6.2,\n 4.5,\n 5.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sepal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.435866284936698,\n \"min\": 2.0,\n \"max\": 4.4,\n \"num_unique_values\": 23,\n \"samples\": [\n 2.3,\n 4.0,\n 3.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.7652982332594667,\n \"min\": 1.0,\n \"max\": 6.9,\n \"num_unique_values\": 43,\n \"samples\": [\n 6.7,\n 3.8,\n 3.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7622376689603465,\n \"min\": 0.1,\n \"max\": 2.5,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.2,\n 1.2,\n 1.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fVIBC6zbPmD_",
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"outputId": "1a834214-4d26-4413-da1f-2a5a9a45b115"
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},
"source": [
"Y = dataset.target\n",
"Y"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nlnZPPJ2Pt8e"
},
"source": [
"### *Splitting Dataset into Train & Test*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WsXx-qmhPzSV",
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"outputId": "3234893f-95d4-477f-fa50-86e38ad8e391"
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},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\n",
"print(X_train.shape)\n",
"print(X_test.shape)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(112, 4)\n",
"(38, 4)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4W9cvXrPR_c5"
},
"source": [
"### *Finding best max_depth Value*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 581
},
"id": "MLko_TEoSOVY",
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"outputId": "932213ba-06aa-49c7-dbba-2ea593edf2d4"
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},
"source": [
"accuracy = []\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"import matplotlib.pyplot as plt\n",
"\n",
"for i in range(1, 10):\n",
" model = DecisionTreeClassifier(max_depth = i, random_state = 0)\n",
" model.fit(X_train, y_train)\n",
" pred = model.predict(X_test)\n",
" score = accuracy_score(y_test, pred)\n",
" accuracy.append(score)\n",
"\n",
"plt.figure(figsize=(12, 6))\n",
"plt.plot(range(1, 10), accuracy, color='red', linestyle='dashed', marker='o',\n",
" markerfacecolor='blue', markersize=10)\n",
"plt.title('Finding best Max_Depth')\n",
"plt.xlabel('pred')\n",
"plt.ylabel('score')"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'score')"
]
},
"metadata": {},
"execution_count": 7
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
],
"image/png": "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
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sthecgH3QI4d"
},
"source": [
"### *Training*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"id": "_1iHKy-0QHm6",
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"outputId": "63f5d1c9-bef7-4778-9a1f-8f2170a90770"
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},
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"model = DecisionTreeClassifier(criterion = 'entropy',max_depth = 3, random_state = 0)\n",
"model.fit(X_train,y_train)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)"
],
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: #000;\n",
" --sklearn-color-text-muted: #666;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: flex;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
" align-items: start;\n",
" justify-content: space-between;\n",
" gap: 0.5em;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label .caption {\n",
" font-size: 0.6rem;\n",
" font-weight: lighter;\n",
" color: var(--sklearn-color-text-muted);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 0.5em;\n",
" text-align: center;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)</pre></div> </div></div></div></div>"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h-dm7-tjXDQO"
},
"source": [
"### *Prediction*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H9IXzjDeXFfv",
2025-02-17 03:49:38 -05:00
"outputId": "0b2faac8-d1d8-4326-eed2-adca7901bbf1"
2025-02-16 12:49:19 -05:00
},
"source": [
"y_pred = model.predict(X_test)\n",
"print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[2 2]\n",
" [1 1]\n",
" [0 0]\n",
" [2 2]\n",
" [0 0]\n",
" [2 2]\n",
" [0 0]\n",
" [1 1]\n",
" [1 1]\n",
" [1 1]\n",
" [2 2]\n",
" [1 1]\n",
" [1 1]\n",
" [1 1]\n",
" [1 1]\n",
" [0 0]\n",
" [1 1]\n",
" [1 1]\n",
" [0 0]\n",
" [0 0]\n",
" [2 2]\n",
" [1 1]\n",
" [0 0]\n",
" [0 0]\n",
" [2 2]\n",
" [0 0]\n",
" [0 0]\n",
" [1 1]\n",
" [1 1]\n",
" [0 0]\n",
" [2 2]\n",
" [1 1]\n",
" [0 0]\n",
" [2 2]\n",
" [2 2]\n",
" [1 1]\n",
" [0 0]\n",
" [2 1]]\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DZ0nCNS_Xg4I"
},
"source": [
"### *Accuracy Score*"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z6zi5kA4XjXx",
2025-02-17 03:49:38 -05:00
"outputId": "93e85e20-69e0-4772-deb5-2e6092cb6940"
2025-02-16 12:49:19 -05:00
},
"source": [
"from sklearn.metrics import accuracy_score\n",
"print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy of the Model: 97.36842105263158%\n"
]
}
]
}
]
}
