good stuff coming
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+44
-111
@@ -2,85 +2,10 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"id": "d2aa9221",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"===== Banknote Authentication =====\n",
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"[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024271844660194164, 0.009708737864077666] [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024271844660194164, 0.009708737864077666]\n"
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]
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},
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
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]
|
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},
|
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"k-NN error after PCA (k=5): 0.1238\n",
|
||||
"Best k via CV: 15\n",
|
||||
"CV accuracy: 0.9993\n",
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"Leave-One-Out error: 0.0015\n",
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"SVM accuracy: 1.0\n",
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"\n",
|
||||
"===== Seeds =====\n",
|
||||
"[0.17460317460317465, 0.17460317460317465, 0.12698412698412698, 0.12698412698412698, 0.15873015873015872, 0.15873015873015872, 0.15873015873015872, 0.1428571428571429] [0.09523809523809523, 0.12698412698412698, 0.12698412698412698, 0.12698412698412698, 0.11111111111111116, 0.1428571428571429, 0.1428571428571429, 0.1428571428571429]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"k-NN error after PCA (k=5): 0.1587\n",
|
||||
"Best k via CV: 11\n",
|
||||
"CV accuracy: 0.9238\n",
|
||||
"Leave-One-Out error: 0.0762\n",
|
||||
"SVM accuracy: 0.873\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Callable, Literal\n",
|
||||
"import numpy as np\n",
|
||||
@@ -92,9 +17,16 @@
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.metrics import accuracy_score\n",
|
||||
"from sklearn.svm import SVC\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from sklearn.svm import SVC\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e4c07993",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ---------- Utility functions ----------\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -121,9 +53,16 @@
|
||||
" df = pd.read_csv(url, sep=r\"\\s+\", names=cols)\n",
|
||||
" X = df.iloc[:, :-1].values\n",
|
||||
" y = df[\"class\"].values\n",
|
||||
" return X, y, \"Seeds\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" return X, y, \"Seeds\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "00c062bd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def knn_error(X, y, k):\n",
|
||||
" Xtr, Xte, ytr, yte = train_test_split(\n",
|
||||
" X, y, test_size=0.3, random_state=42, stratify=y\n",
|
||||
@@ -167,9 +106,16 @@
|
||||
" )\n",
|
||||
" svm = SVC(kernel=kernel, C=1, gamma=\"scale\")\n",
|
||||
" svm.fit(Xtr, ytr)\n",
|
||||
" return svm.score(Xte, yte)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" return svm.score(Xte, yte)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "66947987",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ---------- Plotting ----------\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -190,9 +136,16 @@
|
||||
" plt.title(title)\n",
|
||||
" plt.xlabel(\"PC1\")\n",
|
||||
" plt.ylabel(\"PC2\")\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bf07447a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ---------- Main pipeline ----------\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -242,32 +195,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"id": "bbd5e7a8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"=== Dataset Sizes ===\n",
|
||||
"Banknote: 1372 samples, 4 features\n",
|
||||
"Seeds: 210 samples, 7 features\n",
|
||||
"\n",
|
||||
"=== PCA Explained Variance (2 components) ===\n",
|
||||
"Banknote: 54.5% + 32.3% = 86.8% total\n",
|
||||
"Seeds: 71.9% + 17.1% = 89.0% total\n",
|
||||
"\n",
|
||||
"=== Feature Ranges (min-max span) ===\n",
|
||||
"Banknote: ['13.87', '26.72', '23.21', '11.00']\n",
|
||||
"Seeds: ['10.59', '4.84', '0.11', '1.78', '1.40', '7.69', '2.03']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Additional data for report\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"\n",
|
||||
"# Load datasets\n",
|
||||
"X_bank, y_bank, _ = load_banknote()\n",
|
||||
"X_seeds, y_seeds, _ = load_seeds()\n",
|
||||
|
||||
Reference in new issue
Block a user