{ "cells": [ { "cell_type": "markdown", "id": "617b8878-4306-4353-b50c-6dc6d1b7bba5", "metadata": {}, "source": [ "### Ejemplo de SVM Multiclase Tradicional del tipo categorias vs todo." ] }, { "cell_type": "markdown", "id": "53035bfc-0968-4356-98e8-877d28db5114", "metadata": {}, "source": [ "Un conjunto de datos de la misma categoría vs todos los demas." ] }, { "cell_type": "code", "execution_count": 2, "id": "b4cc273c-8979-45c5-aa4e-152f056ffa22", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import fetch_openml\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Load MNIST\n", "mnist = fetch_openml('mnist_784', version=1, as_frame=False)\n", "X, y = mnist['data'], mnist['target'].astype(int)\n", "\n", "# Normalize\n", "scaler = StandardScaler()\n", "X = scaler.fit_transform(X)\n", "\n", "# --- Create training data: fewer samples for fast training ---\n", "samples_per_class = 300\n", "X_small = []\n", "y_small = []\n", "\n", "for digit in range(10):\n", " idx = np.where(y == digit)[0][:samples_per_class]\n", " X_small.append(X[idx])\n", " y_small.append(y[idx])\n", "\n", "X_small = np.vstack(X_small)\n", "y_small = np.hstack(y_small)\n", "\n", "# --- Create test data ---\n", "test_samples_per_class = 100\n", "X_test = []\n", "y_test = []\n", "\n", "for digit in range(10):\n", " idx = np.where(y == digit)[0][samples_per_class:samples_per_class + test_samples_per_class]\n", " X_test.append(X[idx])\n", " y_test.append(y[idx])\n", "\n", "X_test = np.vstack(X_test)\n", "y_test = np.hstack(y_test)\n", "X_test = scaler.transform(X_test)\n", "\n", "# --- Train one-vs-all SVMs (10 classifiers) ---\n", "classifiers = {}\n", "for digit in range(10):\n", " binary_labels = (y_small == digit).astype(int)\n", " clf = SVC(kernel='linear', probability=True)\n", " clf.fit(X_small, binary_labels)\n", " classifiers[digit] = clf" ] }, { "cell_type": "code", "execution_count": 3, "id": "01a2ab83-1e13-4daf-91fe-087ba90ef8d3", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Accuracy of each one-vs-all SVM on the test data:\n", "SVM for digit 0: Test Accuracy = 90.00%\n", "SVM for digit 1: Test Accuracy = 89.80%\n", "SVM for digit 2: Test Accuracy = 90.00%\n", "SVM for digit 3: Test Accuracy = 90.00%\n", "SVM for digit 4: Test Accuracy = 90.00%\n", "SVM for digit 5: Test Accuracy = 90.60%\n", "SVM for digit 6: Test Accuracy = 90.00%\n", "SVM for digit 7: Test Accuracy = 89.90%\n", "SVM for digit 8: Test Accuracy = 90.00%\n", "SVM for digit 9: Test Accuracy = 90.00%\n" ] } ], "source": [ "print(\"\\nAccuracy of each one-vs-all SVM on the test data:\")\n", "\n", "for digit in range(10):\n", " binary_labels = (y_test == digit).astype(int)\n", " y_pred = classifiers[digit].predict(X_test)\n", " acc = accuracy_score(binary_labels, y_pred)\n", " print(f\"SVM for digit {digit}: Test Accuracy = {acc * 100:.2f}%\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "b0817ace-ff17-4172-9c67-1ebecd3864ce", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Predictions for digit 3 test image:\n" ] }, { "data": { "image/png": "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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 1\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "\n", "Predictions for one image from each other digit (≠ target):\n" ] }, { "data": { "image/png": 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enKlZ542ImD59enGmZiW1lQbSuqo7BQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACANuEG8o1HNOFvtwF/NsFbN0NqqVauKM0899VRxJqJuHHDfvn3FmZpxu87OzuJMV1dXcSYior29vThTM4hXc71bdZyIiPvvv78qV6rm66KV+mtEz50CAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkAbcIF5/jTwdLt3d3cWZmnG7qVOnFmci6ka8aq75+vXrizO1A2OtGqrbs2dPSzK1n+M1g3g1xzrnnHOKMzWf4zWjhRERN910U1WuVO0oZY1Wfd0eDHcKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQOrXQbyawaba0bSjzWWXXVaVq7l+gweXfxrs3LmzOFP7sa0ZIezq6mrJcWrUXO+IukG8QYPK/943bty44kzN9d6wYUNxJiLiv//9b3Gm9pqXauX3r/46ljsFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAklIAILVmJWqA6unpKc7UjFBNnjy5OHP88ccXZyIitm/fXpwZNmxYcaZmYKxmnK02N2TIkOJMzce2ZqSudpyt5jkde+yxxZkLLrigOFMzkPiLX/yiOBNR93XLwXOnAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEDq15XUmtXJmkytVq2kHnfcccWZESNGFGciIjo7O4szGzdubMlxalY+IyLa2tqqcq04Ts3nUO3zqVlknT17dkuO8+STTxZnHnnkkeJMRERHR0dVrlTtqu8b3ZvzWQPwqpQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIAqV8H8WqGv2oH8WqGyVp1nFaO/NWMeO3du7c4U/OcWjkEV5Np1cd22LBhxZmIiCuuuKI4M2nSpOJMzXP6+9//Xpyp/Xyoyb1Zx+1quFIAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBA6tdBvFYOwdVo1fm98MILxZldu3ZVHWvfvn3FmZNPPrk4UzPqtnPnzuJMRER3d/eAzYwYMaI4c/755xdnIiLOPvvs4kzNENyf/vSn4sxvf/vb4kxHR0dxJqLuOdVkagf73ujcKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgCpXwfxavT09FTlBvL43qOPPlqcWbt2bdWxTjnllOLMkCFDijOXXnppceb2228vzkREdHV1FWf27t1bnHnXu95VnKm5DuPGjSvORNSNuu3YsaM489hjjxVnBg8u/1ZSk4lo3bjdQB/R669juVMAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACApBQCSUgAgKQUAUltzkEtyM2fOLH7nNSN13d3dxZmIuiG9mtG0mkxnZ2dxpvY6LF26tDhTc367d+9uSSYiYt++fcWZmhG9GjWjbh0dHVXHev7554szd9xxR3Fmz549xZma5zRs2LDiTETdgGPNx6m9vb04UzOiV3usmkG8FStWvO5j3CkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkMqnAwvUrPjVrgzWLLLWLBPWZGoWGmtXUleuXFmced/73ld1rFI1nw+1uZprXnOcmgXXVatWFWciIh566KHiTM16aasWT2vWTiNat17aqsxA88Z/BgAcNkoBgKQUAEhKAYCkFABISgGApBQASEoBgKQUAEhKAYCkFABISgGA1K+DeDVaOZpWM6xVM+LV09NTnKn14IMPFmceeeSR4sx5551XnKkZj4uIeMc73lGc2bFjR3Hm6aefLs6sXbu2OLNt27biTETEyJEjizM1Q3VDhw4tztQMENZkIupG52q+1mvUfv8aSNwpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAGnADeLVqhnJqlEz4lUzklU7FlaTqxmqe+CBB4ozXV1dxZnaY9Vo1cdp1KhRxZnaY9VkakYfawbnakfqWvW13qrjRAysIT13CgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKACSlAEAacIN4rRyGatXgVc1zqr0OrRp1Gzp0aHGmaZrizKHkSrXq49TKIbijLVOr5lit/LodSNwpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJAG3EpqrVatE9asLfb09BRnapc0a65DzQppTaa7u7s400oDfUmzVcdq1Xpp7XFa9bV+NCye1nCnAEBSCgAkpQBAUgoAJKUAQFIKACSlAEBSCgAkpQBAUgoAJKUAQFIKAKSjZhCvRqsGr2rH7WrUDNXVqBn5GzJkSD+cCQfSqpG/ge5ofE79xZ0CAEkpAJCUAgBJKQCQlAIASSkAkJQCAEkpAJCUAgBJKQCQlAIASSkAkNqaVi2oATDguVMAICkFAJJSACApBQCSUgAgKQUAklIAICkFAJJSACD9H3fU2kER+N5kAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 1\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 1\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 1\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 1\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 1\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 1\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 1\n", "Classifier for 9 predicts: 0\n", "------------------------------\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Classifier for 0 predicts: 0\n", "Classifier for 1 predicts: 0\n", "Classifier for 2 predicts: 0\n", "Classifier for 3 predicts: 0\n", "Classifier for 4 predicts: 0\n", "Classifier for 5 predicts: 0\n", "Classifier for 6 predicts: 0\n", "Classifier for 7 predicts: 0\n", "Classifier for 8 predicts: 0\n", "Classifier for 9 predicts: 1\n", "------------------------------\n" ] } ], "source": [ "target_digit = 3\n", "\n", "# Find a test image of the target digit\n", "target_idx = np.where(y == target_digit)[0][samples_per_class] # offset = training samples used\n", "x_target = X[target_idx]\n", "y_target = y[target_idx]\n", "\n", "print(f\"\\nPredictions for digit {target_digit} test image:\")\n", "plt.imshow(x_target.reshape(28, 28), cmap='gray')\n", "plt.title(f\"True Label: {y_target}\")\n", "plt.axis('off')\n", "plt.show()\n", "\n", "for digit, clf in classifiers.items():\n", " pred = clf.predict([x_target])[0]\n", " print(f\"Classifier for {digit} predicts: {int(pred)}\")\n", "\n", "# --- Show predictions for one sample from each other digit ---\n", "print(\"\\nPredictions for one image from each other digit (≠ target):\")\n", "for other_digit in range(10):\n", " if other_digit == target_digit:\n", " continue\n", " idx = np.where(y == other_digit)[0][samples_per_class]\n", " x_other = X[idx]\n", " y_other = y[idx]\n", "\n", " plt.imshow(x_other.reshape(28, 28), cmap='gray')\n", " plt.title(f\"True Label: {y_other}\")\n", " plt.axis('off')\n", " plt.show()\n", "\n", " for digit, clf in classifiers.items():\n", " pred = clf.predict([x_other])[0]\n", " print(f\"Classifier for {digit} predicts: {int(pred)}\")\n", " print(\"-\" * 30)" ] }, { "cell_type": "markdown", "id": "9e792d54-26af-468f-bd48-93cbfb033512", "metadata": {}, "source": [ "## Ejemplo de un Exemplar SVM" ] }, { "cell_type": "markdown", "id": "1713e73b-cf22-4e19-b3b6-667bfc574399", "metadata": {}, "source": [ "Es 1 muestra vs todo lo demas" ] }, { "cell_type": "code", "execution_count": 11, "id": "d7f76eeb-45d5-4135-9ab1-e420cdffa73d", "metadata": {}, "outputs": [], "source": [ "from sklearn import datasets\n", "from sklearn.svm import LinearSVC\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# 1. Cargar dataset de dígitos (imágenes 8x8)\n", "digits = datasets.load_digits()\n", "\n", "# 2. Seleccionar un ejemplar positivo (un solo \"8\")\n", "positive_idx = np.where(digits.target == 8)[0][0] # primer \"8\"\n", "X_exemplar = digits.images[positive_idx].reshape(1, -1) # 1x64\n", "y_exemplar = [1] # Etiqueta positiva\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "242569a8-d59a-4172-b308-864a93edba93", "metadata": {}, "outputs": [], "source": [ "# 3. Tomar un conjunto de ejemplos negativos (dígitos que no son 8)\n", "neg_indices = np.where(digits.target != 8)[0]\n", "X_negatives = digits.images[neg_indices]\n", "X_negatives = X_negatives.reshape(X_negatives.shape[0], -1)\n", "y_negatives = np.zeros(X_negatives.shape[0]) # Etiquetas negativas (0)\n", "\n", "# 4. Unir positivo y negativos\n", "X_train = np.vstack((X_exemplar, X_negatives))\n", "y_train = np.hstack((y_exemplar, y_negatives))\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "3622dc30-c177-444c-b609-7137c437adbe", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5. Entrenar un SVM lineal (nuestro Exemplar SVM)\n", "svm = LinearSVC(class_weight='balanced', max_iter=10000)\n", "svm.fit(X_train, y_train)\n", "\n", "# 6. Evaluar sobre todo el conjunto de datos\n", "X_all = digits.images.reshape((digits.images.shape[0], -1))\n", "y_scores = svm.decision_function(X_all)\n", "\n", "# 7. Mostrar los 10 dígitos más similares al exemplar\n", "top_indices = np.argsort(y_scores)[-10:]\n", "\n", "plt.figure(figsize=(10, 2))\n", "for i, idx in enumerate(top_indices[::-1]):\n", " plt.subplot(1, 10, i + 1)\n", " plt.imshow(digits.images[idx], cmap='gray')\n", " plt.axis('off')\n", " plt.title(f\"{digits.target[idx]}\")\n", "plt.suptitle(\"Top 10 más similares al exemplar '8'\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "8e6eb600-8ce5-4c69-a26f-ca65c21296ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Evaluación del Exemplar SVM para el dígito '8':\n", "Precisión: 1.00\n", "Recall (sensibilidad): 0.02\n", "F1-score: 0.03\n" ] } ], "source": [ "from sklearn.metrics import precision_score, recall_score, f1_score\n", "\n", "# Crear etiquetas reales: 1 si el dígito es 8, 0 si no\n", "y_true = (digits.target == 8).astype(int)\n", "\n", "# Predicciones binarizadas: 1 si la puntuación del SVM > 0, 0 si no\n", "y_pred = (y_scores > 0).astype(int)\n", "\n", "# Calcular métricas\n", "precision = precision_score(y_true, y_pred)\n", "recall = recall_score(y_true, y_pred)\n", "f1 = f1_score(y_true, y_pred)\n", "\n", "print(\"\\nEvaluación del Exemplar SVM para el dígito '8':\")\n", "print(f\"Precisión: {precision:.2f}\")\n", "print(f\"Recall (sensibilidad): {recall:.2f}\")\n", "print(f\"F1-score: {f1:.2f}\")\n" ] }, { "cell_type": "markdown", "id": "d11386aa-129b-49eb-bf75-3e8631a4dae3", "metadata": {}, "source": [ "Que nos dicen estas métricas,\n", "\n", "- Por un lado, siempre que nuestro modelo diga que ese valor es un 8, realmente si es un 8, o sea que no hay valores falsos positivos (precision).\n", "- Respecto al recall, nos queda bajo, indicando que hay muchos valores que si eran 8 y no los clasifico como un 8, esto se puede deber a que, tenemos solo 1 valor 8 del cual aprende nuestro valor.\n", "- el f1 score nos dice que la relacion entre el recall y la precision es dispareja, o sea, el recall es muy bajo" ] }, { "cell_type": "markdown", "id": "0e9a821e-c7fc-4f4a-b5ef-87d838f79a4b", "metadata": {}, "source": [ "Una posible solucion es entrenar varios exemplar svm para el mismo número y ver una ponderación de estos. Ya que dependiendo el 8 que le entreguemos es como discriminará los otros números." ] }, { "cell_type": "markdown", "id": "c0f60e16-f891-48bb-892b-db25cb3722d1", "metadata": {}, "source": [ "### ¿ Qué pasa con el accuracy?, ¿Mejorará o disminuirá respecto al SVM tradicional?" ] }, { "cell_type": "code", "execution_count": 15, "id": "55080685-50f5-49e5-9591-3380b3dd45f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy del Exemplar SVM para el dígito '8': 0.90\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "accuracy = accuracy_score(y_true, y_pred)\n", "print(f\"Accuracy del Exemplar SVM para el dígito '8': {accuracy:.2f}\")\n" ] }, { "cell_type": "markdown", "id": "da7959df-f523-45b2-8724-d7a99d3d1e67", "metadata": {}, "source": [ "### Por qué el accuracy es tan alto?, si sabemos que el recall es bajo o sea que nuestro modelo el 98% de las veces que si era \"8\" las clasifica como si no fueran un \"8\"." ] }, { "cell_type": "markdown", "id": "fd889ce7-3ecc-42ed-82d5-24d4e70d5479", "metadata": {}, "source": [ "# Respuesta:" ] }, { "cell_type": "markdown", "id": "087dcd46-acee-496f-9240-7a0a9210b5d4", "metadata": {}, "source": [ "- TP = 3 → Predijo 8 y era 8 (correcto)\n", "- FN = 175 → Era 8 pero no lo predijo como tal\n", "- TN ≈ 1616 → Era no-8 y predijo no-8 (correcto)\n", "- FP = 0 → No hay falsos positivos\n" ] }, { "attachments": { "6f16f491-d06b-48b8-a8cd-45f8421322a6.png": { "image/png": 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" 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