forked from zll0622/Modeling_Analysis
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5893b1c0-fa37-40b0-bf86-5e542987634e",
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"metadata": {},
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"source": [
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"# 1 获取数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e5e89d3b-2384-4bbf-9197-2dbbca120838",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([0, 1, 2, ..., 8, 9, 8])"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.datasets import load_digits\n",
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"digits = load_digits() # 加载数据\n",
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"data = digits[\"data\"] # 属性列\n",
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"target = digits[\"target\"] # 标签列\n",
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"target"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "950a6379-974e-4e74-82b2-f58a8606682a",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "8fac6553-56ad-4096-834a-55bd9f6cdab5",
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"metadata": {},
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"source": [
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"# 2 划分数据集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "86506fed-8cb6-4e54-88f4-a70150297947",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"x_train,x_test,y_train,y_test=train_test_split(data, target, test_size=0.2, random_state=123)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3dd4bc37-cf06-41f2-a16b-b4851e497c8d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "e021dded-b3cb-4c04-bab2-e18f5374c037",
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"metadata": {},
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"source": [
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"# 3 模型构建"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "525e6d7e-0140-4443-818f-71ce37baee79",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"model= KNeighborsClassifier(n_neighbors=5).fit(x_train,y_train) # 模型构建\n",
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"y_pred=model.predict(x_test) #预测\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5766e641-8f3f-4a80-83d7-3edb3fa4a2b0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([3, 3, 4, 4, 1, 3, 1, 0, 7, 4, 0, 0, 5, 1, 6, 0, 3, 1, 0, 6, 9, 7,\n",
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" 7, 5, 4, 5, 7, 0, 0, 9, 1, 6, 8, 6, 1, 4, 6, 2, 1, 6, 0, 6, 2, 0,\n",
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" 2, 3, 9, 8, 8, 1, 2, 8, 6, 5, 1, 6, 4, 6, 0, 4, 3, 5, 6, 2, 2, 8,\n",
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" 8, 2, 0, 1, 7, 9, 4, 7, 3, 9, 4, 3, 3, 8, 3, 5, 4, 6, 1, 2, 4, 2,\n",
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" 4, 5, 8, 4, 1, 7, 6, 7, 5, 5, 6, 3, 9, 4, 6, 6, 8, 1, 7, 5, 6, 4,\n",
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" 9, 8, 7, 0, 9, 0, 2, 9, 0, 5, 1, 4, 4, 2, 2, 0, 7, 3, 6, 8, 2, 5,\n",
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" 2, 3, 3, 9, 6, 7, 4, 0, 7, 3, 2, 2, 2, 3, 8, 1, 7, 1, 5, 7, 9, 1,\n",
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" 3, 2, 6, 0, 7, 9, 6, 0, 2, 6, 3, 1, 5, 5, 9, 6, 1, 8, 6, 1, 3, 4,\n",
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" 4, 2, 0, 5, 9, 0, 7, 0, 7, 5, 8, 4, 8, 8, 3, 7, 9, 8, 2, 0, 3, 4,\n",
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" 9, 4, 1, 8, 7, 0, 5, 4, 5, 2, 2, 1, 4, 2, 0, 8, 7, 3, 2, 9, 2, 3,\n",
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" 8, 0, 6, 0, 2, 8, 7, 5, 4, 8, 0, 1, 5, 5, 0, 9, 8, 7, 7, 1, 4, 2,\n",
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" 4, 7, 1, 3, 0, 8, 2, 8, 6, 6, 3, 5, 6, 4, 5, 4, 6, 5, 7, 7, 9, 9,\n",
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" 1, 0, 4, 6, 1, 7, 3, 9, 8, 1, 0, 4, 5, 6, 8, 2, 5, 8, 4, 0, 8, 3,\n",
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" 1, 6, 6, 4, 6, 1, 4, 6, 6, 4, 1, 5, 3, 0, 1, 6, 0, 2, 5, 5, 8, 8,\n",
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" 2, 6, 0, 1, 1, 5, 1, 6, 9, 2, 8, 2, 4, 9, 8, 3, 3, 3, 2, 4, 0, 9,\n",
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" 4, 3, 6, 0, 2, 1, 3, 3, 2, 5, 9, 0, 7, 5, 9, 7, 6, 4, 7, 9, 6, 7,\n",
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" 4, 1, 5, 9, 6, 5, 5, 0])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y_pred"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6469074a-0502-43ae-a076-16d3f744ce22",
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"metadata": {},
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"source": [
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"# 4 模型评价"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "efd03e71-1992-4a5a-871f-2d544230a17d",
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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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"Confusion Matrix:\n",
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"[[39 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 34 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 36 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 33 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 41 0 0 1 0 0]\n",
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" [ 0 0 0 0 0 36 0 0 0 1]\n",
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" [ 0 0 0 0 0 0 43 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 31 0 0]\n",
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" [ 0 3 1 0 0 0 0 0 33 0]\n",
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" [ 0 0 0 1 0 0 0 0 0 27]]\n",
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"k邻近模型预测的正确率为： 0.9805555555555555\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import confusion_matrix, accuracy_score \n",
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" \n",
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"\n",
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"# 计算混淆矩阵 \n",
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"cm = confusion_matrix(y_test, y_pred)\n",
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"print(\"Confusion Matrix:\")\n",
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"print(cm)\n",
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"\n",
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"\n",
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"print(\"k邻近模型预测的正确率为：\",accuracy_score(y_test,y_pred))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "37b7d2de-80da-4c41-99af-0aae3cc5d4ce",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3609a180-6bc6-4b76-8269-7d620d45c2ce",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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