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@PandoraRiot
Created March 5, 2025 22:22
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RegresiónLog.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyONu+eCpOn93scsRGPWqyaV",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/PandoraRiot/e34f3fa13da21d934e92fb0eeda6e4d9/regresi-nlog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "weYX1aw0HyVA"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.datasets import make_classification"
]
},
{
"cell_type": "code",
"source": [
"\n",
"X, y = make_classification(n_samples = 500 , n_features = 2 , n_redundant = 0, n_informative = 2, n_clusters_per_class = 1, n_classes = 2, random_state = 8)\n",
"\n",
"print (X.shape, y.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3QAlHN7rH8j_",
"outputId": "3396f995-d14c-4997-e463-c777bbd9642a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(500, 2) (500,)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Colores: https://matplotlib.org/stable/gallery/color/named_colors.html Y \n",
"https://matplotlib.org/stable/users/explain/colors/colormaps.html\n",
"\n",
"```\n",
"# Tiene formato de código\n",
"```\n",
"\n"
],
"metadata": {
"id": "RogyoSl-QjcJ"
}
},
{
"cell_type": "code",
"source": [
"_, ax = plt.subplots(figsize=( 5,4 ))\n",
"ax.scatter( X[:,0] , X[:,1] , c = y , edgecolors= 'k', cmap = 'Paired')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 385
},
"id": "k5kaTD7LLD5Q",
"outputId": "f829b93d-4c4c-4067-ae54-2d62bc4b7258"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x79cda48ee610>"
]
},
"metadata": {},
"execution_count": 30
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 500x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Create an instance\n",
"logistic_regression = LogisticRegression()\n",
"\n",
"# Train the model\n",
"logistic_regression.fit ( X , y ) #(# Samples, # Features)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
},
"id": "wZOnjv1vPf0B",
"outputId": "941ac428-1d8c-4a2d-834b-2336c458322a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression()"
],
"text/html": [
"<style>#sk-container-id-2 {\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-2 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-2 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-2 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-2 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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
]
},
"metadata": {},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"source": [
"print(logistic_regression.coef_)\n",
"print(logistic_regression.intercept_)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VM6kPn2QRYKR",
"outputId": "e39f5bda-2877-4983-e994-5762142cf337"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[-2.05708959 1.85247996]]\n",
"[-1.61624314]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Decision Boundaries"
],
"metadata": {
"id": "lK4mGnUtSQpI"
}
},
{
"cell_type": "code",
"source": [
"# Min-Max values for the grid of decision boundaries\n",
"h_min, h_max = min(X[:, 0])-offset, max(X[:, 0])+offset # Para la primera característica (feature)\n",
"v_min, v_max = min(X[:, 1])-offset, max(X[:, 1])+offset # Para la segunda característica (feature)\n",
"#X[:, 0]: Selecciona todos los valores de la primera característica (la columna 0) de tu conjunto de datos X.\n",
"#X[:, 1]: Selecciona todos los valores de la segunda característica (la columna 1) de tu conjunto de datos X.\n",
"#min(X[:, 0]) y max(X[:, 0]): Calculan el valor mínimo y máximo para la primera característica (usado para establecer los límites de la horizontal).\n",
"#min(X[:, 1]) y max(X[:, 1]): Calculan el valor mínimo y máximo para la segunda característica (usado para establecer los límites de la vertical).\n",
"offset = 0.5\n",
"# Create grid\n",
"h_grid, v_grid = np.meshgrid(np.linspace(h_min ,h_max,100), np.linspace(v_min,v_max, 100) )\n",
"print(h_grid.shape, v_grid.shape)\n",
"print(h_grid.ravel().shape, v_grid.ravel().shape)\n",
"print(np.concatenate((h_grid.ravel().reshape(-1, 1), v_grid.ravel().reshape(-1, 1)), axis=1).shape)\n",
"#.ravel() convierte las matrices 2D en un arreglo 1D (un vector), es decir, \"aplana\" las matrices.\n",
"print(np.c_[h_grid.ravel(), v_grid.ravel()].shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hmm2D36qSQWi",
"outputId": "e00eb011-a224-4e38-ae59-9dd057d5bdd0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(100, 100) (100, 100)\n",
"(10000,) (10000,)\n",
"(10000, 2)\n",
"(10000, 2)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Estimate predictions over the grid\n",
"pred_grid = logistic_regression.predict(np.c_[h_grid.ravel(), v_grid.ravel()])\n",
"print (pred_grid.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TFZUSKNjUhpk",
"outputId": "e7b0e963-84d1-4c58-e613-7005ca83d3b3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(10000,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pred_grid = pred_grid.reshape(h_grid.shape)\n",
"print(pred_grid.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q1Qaq7qMYImt",
"outputId": "2daf9f6a-5639-4db6-9df4-f02b2c7436a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(100, 100)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"# Draw grid\n",
"_, ax = plt.subplots (figsize=(6,5))\n",
"ax.pcolormesh( h_grid , v_grid , pred_grid , cmap = \"Paired\")\n",
"\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 462
},
"id": "JIZ95ZpHYYAR",
"outputId": "b5dbf9b4-3966-48c5-bafd-e71e3b660f20"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x79cda49cd1d0>"
]
},
"metadata": {},
"execution_count": 56
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x500 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Draw grid\n",
"_, ax = plt.subplots (figsize=(6,5))\n",
"ax.pcolormesh( h_grid , v_grid , pred_grid , cmap = \"Paired\")\n",
"# Scatter real values\n",
"\n",
"ax.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=\"Paired\")\n",
"\n",
"ax.set_xticks(())\n",
"ax.set_yticks(())\n",
"#Aquí se accede a todas las filas de la primera (índice 0) y la segunda (índice 1) columna de X, que corresponden a las dos características para el gráfico de dispersión.\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 439
},
"id": "TcXNzGU2ZD0A",
"outputId": "d5246811-684e-4dc5-b5c0-d2984c4cef0a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[]"
]
},
"metadata": {},
"execution_count": 57
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Real data"
],
"metadata": {
"id": "tMUnEbAqbSYV"
}
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.datasets import load_iris"
],
"metadata": {
"id": "Sad84aooZDrp"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X,y = load_iris(return_X_y=True)\n",
"print(X.shape, y.shape )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "l2Caq6TbbUbD",
"outputId": "4ca3a759-dd13-40fb-e6fd-55fac8174a1e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(150, 4) (150,)\n"
]
}
]
}
]
}
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