{
"cells": [
{
"cell_type": "markdown",
"id": "7a797e37",
"metadata": {},
"source": [
"# Nearest Neighbors\n",
"In this section we'll develop the *nearest neighbor* method of classification. Just focus on the ideas for now and don't worry if some of the code is mysterious. Later in the chapter we'll see how to organize our ideas into code that performs the classification."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "728d3859",
"metadata": {
"execution": {
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"tags": [
"hide-input",
"thebe-init",
"runnable"
]
},
"outputs": [],
"source": [
"path_data = '../../data/'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import math\n",
"import scipy.stats as stats\n",
"\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('fivethirtyeight')\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"id": "69125490",
"metadata": {},
"source": [
"```{contents}\n",
":local:\n",
":depth: 2\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2a6e09b4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.833280Z",
"iopub.status.busy": "2026-08-31T06:02:45.833178Z",
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"shell.execute_reply": "2026-08-31T06:02:45.834534Z"
},
"tags": []
},
"outputs": [],
"source": [
"def standard_units(x):\n",
" return (x - np.mean(x))/np.std(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e97c9d81",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.835957Z",
"iopub.status.busy": "2026-08-31T06:02:45.835895Z",
"iopub.status.idle": "2026-08-31T06:02:45.838188Z",
"shell.execute_reply": "2026-08-31T06:02:45.838019Z"
},
"tags": []
},
"outputs": [],
"source": [
"def distance(point1, point2):\n",
" \"\"\"he Euclidean distance between two arrays of numbers.\"\"\"\n",
" return np.sqrt(np.sum((point1 - point2)**2))\n",
"\n",
"def all_distances(training, point):\n",
" \"\"\"\n",
" The distance between p (an array of numbers) and each row in training.\n",
" training: dataframe including features and 'Class'\n",
" point: np.array of feature values\n",
" \"\"\"\n",
" attributes = training.drop(columns=['Class'])\n",
" return np.sqrt(np.sum((attributes.values - point) ** 2, axis=1))\n",
"\n",
"def table_with_distances(training, point):\n",
" \"\"\"Return a copy of training with a new column of distances.\"\"\"\n",
" training_copy = training.copy()\n",
" training_copy['Distance'] = all_distances(training, point)\n",
" return training_copy\n",
"\n",
"def closest(training, point, k):\n",
" \"\"\"Return the k closest rows in training to point p.\"\"\"\n",
" with_dists = table_with_distances(training, point)\n",
" sorted_by_dist = with_dists.sort_values('Distance')\n",
" return sorted_by_dist.head(k)\n",
"\n",
"# def majority(topk_classes):\n",
"def majority(nearest):\n",
" \"\"\"Return 1 if majority of Class column is 1, otherwise 0.\"\"\"\n",
" # ones = np.sum(topk_classes['Class'] == 1)\n",
" # zeros = np.sum(topk_classes['Class'] == 0)\n",
" # # return 1 if ones > zeros else 0\n",
" # if ones > zeros:\n",
" # return 1\n",
" # else:\n",
" # return 0\n",
"\n",
" return nearest['Class'].mode().iloc[0] ### mode: most common values (1, 0)\n",
" # or: return nearest['Class'].value_counts().idxmax()\n",
"\n",
"def classify(training, p, k):\n",
" \"\"\"K-nearest neighbor classification for test point.\"\"\"\n",
" nearest = closest(training, p, k)\n",
" return majority(nearest)"
]
},
{
"cell_type": "markdown",
"id": "8c3eb0b9",
"metadata": {},
"source": [
"## Chronic kidney disease\n",
"\n",
"Let's work through an example. We're going to work with a data set that was collected to help doctors diagnose chronic kidney disease (CKD). Each row in the data set represents a single patient who was treated in the past and whose diagnosis is known. For each patient, we have a bunch of measurements from a blood test. We'd like to find which measurements are most useful for diagnosing CKD, and develop a way to classify future patients as \"has CKD\" or \"doesn't have CKD\" based on their blood test results."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "31f69a88",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.839201Z",
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" Age Blood Pressure Specific Gravity Albumin Sugar Red Blood Cells \\\n",
"0 48 70 1.005 4 0 normal \n",
"1 53 90 1.020 2 0 abnormal \n",
"2 63 70 1.010 3 0 abnormal \n",
"3 68 80 1.010 3 2 normal \n",
"4 61 80 1.015 2 0 abnormal \n",
".. ... ... ... ... ... ... \n",
"153 55 80 1.020 0 0 normal \n",
"154 42 70 1.025 0 0 normal \n",
"155 12 80 1.020 0 0 normal \n",
"156 17 60 1.025 0 0 normal \n",
"157 58 80 1.025 0 0 normal \n",
"\n",
" Pus Cell Pus Cell clumps Bacteria Glucose ... Packed Cell Volume \\\n",
"0 abnormal present notpresent 117 ... 32 \n",
"1 abnormal present notpresent 70 ... 29 \n",
"2 abnormal present notpresent 380 ... 32 \n",
"3 abnormal present present 157 ... 16 \n",
"4 abnormal notpresent notpresent 173 ... 24 \n",
".. ... ... ... ... ... ... \n",
"153 normal notpresent notpresent 140 ... 47 \n",
"154 normal notpresent notpresent 75 ... 54 \n",
"155 normal notpresent notpresent 100 ... 49 \n",
"156 normal notpresent notpresent 114 ... 51 \n",
"157 normal notpresent notpresent 131 ... 53 \n",
"\n",
" White Blood Cell Count Red Blood Cell Count Hypertension \\\n",
"0 6700 3.9 yes \n",
"1 12100 3.7 yes \n",
"2 4500 3.8 yes \n",
"3 11000 2.6 yes \n",
"4 9200 3.2 yes \n",
".. ... ... ... \n",
"153 6700 4.9 no \n",
"154 7800 6.2 no \n",
"155 6600 5.4 no \n",
"156 7200 5.9 no \n",
"157 6800 6.1 no \n",
"\n",
" Diabetes Mellitus Coronary Artery Disease Appetite Pedal Edema Anemia \\\n",
"0 no no poor yes yes \n",
"1 yes no poor no yes \n",
"2 yes no poor yes no \n",
"3 yes yes poor yes no \n",
"4 yes yes poor yes yes \n",
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ckd = pd.read_csv(path_data + 'ckd.csv')\n",
"ckd.rename(columns={'Blood Glucose Random':'Glucose'}, inplace=True)\n",
"ckd"
]
},
{
"cell_type": "markdown",
"id": "4e32af4f",
"metadata": {},
"source": [
"Some of the variables are categorical (words like \"abnormal\"), and some quantitative. The quantitative variables all have different scales. We're going to want to make comparisons and estimate distances, often by eye, so let's select just a few of the variables and work in standard units. Then we won't have to worry about the scale of each of the different variables."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "06ba7441",
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"tags": []
},
"outputs": [
{
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" Hemoglobin Glucose White Blood Cell Count Class\n",
"0 -0.865744 -0.221549 -0.569768 1\n",
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"2 -1.004968 3.841231 -1.275582 1\n",
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},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ckd_su = pd.DataFrame({\n",
" 'Hemoglobin': standard_units(ckd['Hemoglobin']), \n",
" 'Glucose': standard_units(ckd['Glucose']), \n",
" 'White Blood Cell Count': standard_units(ckd['White Blood Cell Count']), \n",
" 'Class': ckd['Class'].astype(int)\n",
"})\n",
"ckd_su"
]
},
{
"cell_type": "markdown",
"id": "14159914",
"metadata": {},
"source": [
"Let's look at two columns in particular: the hemoglobin level (in the patient's blood), and the blood glucose level (at a random time in the day; without fasting specially for the blood test). \n",
"\n",
"We'll draw a scatter plot to visualize the relation between the two variables. Blue dots are patients with CKD; gold dots are patients without CKD. What kind of medical test results seem to indicate CKD?"
]
},
{
"cell_type": "markdown",
"id": "832a7dfb",
"metadata": {},
"source": [
"Previously, to create a df containing the required columns we have used the join function, in this example we will use the merge function.\n",
"\n",
"[pandas.merge](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "80684517",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.857568Z",
"iopub.status.busy": "2026-08-31T06:02:45.857510Z",
"iopub.status.idle": "2026-08-31T06:02:45.859819Z",
"shell.execute_reply": "2026-08-31T06:02:45.859664Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"glucose_color_darkblue = ckd_combined[ckd_combined['Color'] == 'darkblue']\n",
"glucose_color_gold = ckd_combined[ckd_combined['Color'] == 'gold']\n",
"\n",
"fig, ax = plt.subplots(figsize=(4,4))\n",
"\n",
"ax.scatter(glucose_color_darkblue['Hemoglobin'], \n",
" glucose_color_darkblue['Glucose'], \n",
" label='Color=darkblue (CKD)',\n",
" color='darkblue', s=10)\n",
"\n",
"ax.scatter(glucose_color_gold['Hemoglobin'], \n",
" glucose_color_gold['Glucose'], \n",
" label='Color=gold', \n",
" color='gold', s=10)\n",
"\n",
"x_label = 'Hemoglobin'\n",
"y_label = 'Glucose'\n",
"plt.xlabel(x_label)\n",
"y_vals = ax.get_yticks()\n",
"plt.ylabel(y_label)\n",
"ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "e36917c0",
"metadata": {},
"source": [
"Suppose Alice is a new patient who is not in the data set. If I tell you Alice's hemoglobin level and blood glucose level, could you predict whether she has CKD? It sure looks like it! You can see a very clear pattern here: points in the lower-right tend to represent people who don't have CKD, and the rest tend to be folks with CKD. To a human, the pattern is obvious. But how can we program a computer to automatically detect patterns such as this one?"
]
},
{
"cell_type": "markdown",
"id": "d52a59e7",
"metadata": {},
"source": [
"## A Nearest Neighbor Classifier\n",
"\n",
"There are lots of kinds of patterns one might look for, and lots of algorithms for classification. But I'm going to tell you about one that turns out to be surprisingly effective. It is called *nearest neighbor classification*. Here's the idea. If we have Alice's hemoglobin and glucose numbers, we can put her somewhere on this scatterplot; the hemoglobin is her x-coordinate, and the glucose is her y-coordinate. Now, to predict whether she has CKD or not, we find the nearest point in the scatterplot and check whether it is blue or gold; we predict that Alice should receive the same diagnosis as that patient.\n",
"\n",
"In other words, to classify Alice as CKD or not, we find the patient in the training set who is \"nearest\" to Alice, and then use that patient's diagnosis as our prediction for Alice. The intuition is that if two points are near each other in the scatterplot, then the corresponding measurements are pretty similar, so we might expect them to receive the same diagnosis (more likely than not). We don't know Alice's diagnosis, but we do know the diagnosis of all the patients in the training set, so we find the patient in the training set who is most similar to Alice, and use that patient's diagnosis to predict Alice's diagnosis."
]
},
{
"cell_type": "markdown",
"id": "d902ded9",
"metadata": {},
"source": [
"In the graph below, the red dot represents Alice. It is joined with a black line to the point that is nearest to it – its *nearest neighbor* in the training set. The figure is drawn by a function called `show_closest`. It takes an array that represents the $x$ and $y$ coordinates of Alice's point. Vary those to see how the closest point changes! Note especially when the closest point is blue and when it is gold."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1098946f",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.909981Z",
"iopub.status.busy": "2026-08-31T06:02:45.909917Z",
"iopub.status.idle": "2026-08-31T06:02:45.912358Z",
"shell.execute_reply": "2026-08-31T06:02:45.912187Z"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def show_closest(point):\n",
" \"\"\"\n",
" point = np.array([x, y])\n",
" Shows the point in red and draws a line to the closest dataset point\n",
" \"\"\"\n",
" ### get the features from pandas DataFrame\n",
" HemoGl = ckd_combined[['Hemoglobin', 'Glucose', 'Color']]\n",
" \n",
" ### calculate distances from point to all points\n",
" from scipy.spatial.distance import cdist\n",
" coords = ckd_combined[['Hemoglobin', 'Glucose']].values\n",
" distances = cdist([point], coords)[0]\n",
" \n",
" ### get index of closest point\n",
" closest_idx = np.argmin(distances)\n",
" \n",
" ### get closest point coordinates\n",
" x_closest = ckd_combined.iloc[closest_idx]['Hemoglobin']\n",
" y_closest = ckd_combined.iloc[closest_idx]['Glucose']\n",
" \n",
" ### create scatter plot grouped by color\n",
" fig, ax = plt.subplots(figsize=(4, 4))\n",
"\n",
" color_map = {\n",
" 'gold': 'gold',\n",
" 'darkblue': 'darkblue'\n",
" }\n",
" \n",
" for color_group in ckd_combined['Color'].unique():\n",
" subset = ckd_combined[ckd_combined['Color'] == color_group]\n",
" ax.scatter(subset['Hemoglobin'], subset['Glucose'], \n",
" color=color_map.get(color_group, 'gray'),\n",
" label=color_group, alpha=0.6, s=10)\n",
" \n",
" ### plot the query point in red\n",
" ax.scatter(point[0], point[1], color='red', s=30, zorder=5)\n",
" \n",
" ### draw line to closest point\n",
" ax.plot([point[0], x_closest], [point[1], y_closest], \n",
" color='k', lw=2, zorder=4) \n",
" \n",
" ax.set_xlabel('Hemoglobin')\n",
" ax.set_ylabel('Glucose')\n",
" ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "31c5f4d6",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.913247Z",
"iopub.status.busy": "2026-08-31T06:02:45.913192Z",
"iopub.status.idle": "2026-08-31T06:02:45.945239Z",
"shell.execute_reply": "2026-08-31T06:02:45.945020Z"
},
"tags": []
},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# In this example, Alice's Hemoglobin attribute is 0 and her Glucose is 1.5.\n",
"alice = np.array([0, 1.5])\n",
"show_closest(alice)"
]
},
{
"cell_type": "markdown",
"id": "7c26dc44",
"metadata": {},
"source": [
"Thus our *nearest neighbor classifier* works like this:\n",
"- Find the point in the training set that is nearest to the new point.\n",
"- If that nearest point is a \"CKD\" point, **classify** the new point as \"CKD\". If the nearest point is a \"not CKD\" point, classify the new point as \"not CKD\".\n",
"\n",
"The scatterplot suggests that this nearest neighbor classifier should be pretty accurate. Points in the lower-right will tend to receive a \"no CKD\" diagnosis, as their nearest neighbor will be a gold point. The rest of the points will tend to receive a \"CKD\" diagnosis, as their nearest neighbor will be a blue point. So the nearest neighbor strategy seems to capture our intuition pretty well, for this example."
]
},
{
"cell_type": "markdown",
"id": "e4fc4acb",
"metadata": {},
"source": [
"## Decision boundary\n",
"\n",
"Sometimes a helpful way to visualize a classifier is to map out the kinds of attributes where the classifier would predict 'CKD', and the kinds where it would predict 'not CKD'. We end up with some boundary between the two, where points on one side of the boundary will be classified 'CKD' and points on the other side will be classified 'not CKD'. This boundary is called the *decision boundary*. Each different classifier will have a different decision boundary; the decision boundary is just a way to visualize what criteria the classifier is using to classify points.\n",
"\n",
"For example, suppose the coordinates of Alice's point are (0, 1.5). Notice that the nearest neighbor is blue. Now try reducing the height (the $y$-coordinate) of the point. You'll see that at around $y = 0.95$ the nearest neighbor turns from blue to gold."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f461a244",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:45.946360Z",
"iopub.status.busy": "2026-08-31T06:02:45.946302Z",
"iopub.status.idle": "2026-08-31T06:02:45.975374Z",
"shell.execute_reply": "2026-08-31T06:02:45.975162Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(4,4))\n",
"\n",
"test_grid.plot.scatter('Hemoglobin', 'Glucose', color='red', alpha=0.3, s=30)\n",
"\n",
"plt.scatter(ckd_combined['Hemoglobin'], ckd_combined['Glucose'], \n",
" c=ckd_combined['Color'], edgecolor='k')\n",
"\n",
"plt.xlim(-2, 2)\n",
"plt.ylim(-2, 2);"
]
},
{
"cell_type": "markdown",
"id": "ec50b133",
"metadata": {},
"source": [
"Each of the red points has a **nearest neighbor** in the training set (the same blue and gold points as before). For some red points you can easily tell whether the nearest neighbor is blue or gold. For others, it's a little more tricky to make the decision by eye. Those are the points near the decision boundary.\n",
"\n",
"But the computer can easily determine the nearest neighbor of each point. So let's get it to apply our nearest neighbor classifier to each of the red points: \n",
"\n",
"For each red point, it must find the closest point in the training set; it must then change the color of the red point to become the color of the nearest neighbor. \n",
"\n",
"The resulting graph shows which points will get classified as 'CKD' (all the blue ones), and which as 'not CKD' (all the gold ones)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7f590823",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.037812Z",
"iopub.status.busy": "2026-08-31T06:02:46.037738Z",
"iopub.status.idle": "2026-08-31T06:02:46.039271Z",
"shell.execute_reply": "2026-08-31T06:02:46.039088Z"
},
"tags": []
},
"outputs": [],
"source": [
"def classify_grid(training, test, k):\n",
" c = np.array([])\n",
" for _, row in test.iterrows(): ### _ is the unused index variable; iterrows: returns index + series\n",
" # Run the classifier on the ith patient in the test set\n",
" point = row.values\n",
" c = np.append(c, classify(training, point, k)) \n",
" return c"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "fba51708",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.040174Z",
"iopub.status.busy": "2026-08-31T06:02:46.040118Z",
"iopub.status.idle": "2026-08-31T06:02:46.454343Z",
"shell.execute_reply": "2026-08-31T06:02:46.453953Z"
},
"tags": []
},
"outputs": [],
"source": [
"c = classify_grid(ckd_combined.drop(columns=['White Blood Cell Count', 'Color']), test_grid[['Hemoglobin', 'Glucose']], 1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "556aa010",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.456574Z",
"iopub.status.busy": "2026-08-31T06:02:46.456470Z",
"iopub.status.idle": "2026-08-31T06:02:46.460379Z",
"shell.execute_reply": "2026-08-31T06:02:46.459693Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., ..., 1., 1., 1.], shape=(1681,))"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "625a5dca",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.462948Z",
"iopub.status.busy": "2026-08-31T06:02:46.462753Z",
"iopub.status.idle": "2026-08-31T06:02:46.471978Z",
"shell.execute_reply": "2026-08-31T06:02:46.469219Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" Hemoglobin Glucose Class Color\n",
"0 -2.0 -2.0 1 darkblue\n",
"1 -2.0 -1.9 1 darkblue\n",
"2 -2.0 -1.8 1 darkblue\n",
"3 -2.0 -1.7 1 darkblue\n",
"4 -2.0 -1.6 1 darkblue\n",
"... ... ... ... ...\n",
"1676 2.0 1.6 1 darkblue\n",
"1677 2.0 1.7 1 darkblue\n",
"1678 2.0 1.8 1 darkblue\n",
"1679 2.0 1.9 1 darkblue\n",
"1680 2.0 2.0 1 darkblue\n",
"\n",
"[1681 rows x 4 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"color_table['Class'] = color_table['Class'].astype(int)\n",
"test_grid2 = test_grid[['Hemoglobin', 'Glucose', 'Class']].merge(\n",
" color_table, on='Class', how='left'\n",
")\n",
"test_grid2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "097824b4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.530237Z",
"iopub.status.busy": "2026-08-31T06:02:46.530169Z",
"iopub.status.idle": "2026-08-31T06:02:46.585343Z",
"shell.execute_reply": "2026-08-31T06:02:46.584874Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_grid3 = test_grid2[test_grid2['Color'] == 'darkblue'] # predicted darkblue\n",
"test_grid4 = test_grid2[test_grid2['Color'] == 'gold'] # predicted gold\n",
"test_grid5 = ckd_combined[ckd_combined['Color'] == 'darkblue']\n",
"test_grid6 = ckd_combined[ckd_combined['Color'] == 'gold']\n",
"\n",
"fig, ax = plt.subplots(figsize=(4,4))\n",
"\n",
"ax.scatter(test_grid3['Hemoglobin'], \n",
" test_grid3['Glucose'], \n",
" color='darkblue', alpha=0.4, s=30)\n",
"\n",
"ax.scatter(test_grid4['Hemoglobin'], \n",
" test_grid4['Glucose'], \n",
" color='gold', alpha=0.4, s=30)\n",
"\n",
"ax.scatter(test_grid5['Hemoglobin'], \n",
" test_grid5['Glucose'], \n",
" color='darkblue', label='Color=darkblue', ec='darkblue', s=30)\n",
"\n",
"ax.scatter(test_grid6['Hemoglobin'], \n",
" test_grid6['Glucose'], \n",
" color='gold', label='Color=gold', ec='darkblue', s=30)\n",
"\n",
"x_label = 'Hemoglobin'\n",
"y_label = 'Glucose'\n",
"plt.xlabel(x_label)\n",
"plt.ylabel(y_label)\n",
"ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
"plt.xlabel(x_label)\n",
"plt.xlim(-2, 2)\n",
"plt.ylim(-2, 2)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6b927985",
"metadata": {},
"source": [
"The **decision boundary** is where the classifier switches from turning the red points blue to turning them gold."
]
},
{
"cell_type": "markdown",
"id": "a4ba936a",
"metadata": {},
"source": [
"## k-Nearest Neighbors\n",
"\n",
"However, the separation between the two classes won't always be quite so **clean**. For instance, suppose that instead of hemoglobin levels we were to look at **white blood cell** count. Look at what happens:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e5f41670",
"metadata": {
"execution": {
"iopub.execute_input": "2026-08-31T06:02:46.588123Z",
"iopub.status.busy": "2026-08-31T06:02:46.587910Z",
"iopub.status.idle": "2026-08-31T06:02:46.624395Z",
"shell.execute_reply": "2026-08-31T06:02:46.624186Z"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"glucose_color_darkblue = ckd_combined[ckd_combined['Color'] == 'darkblue']\n",
"glucose_color_gold = ckd_combined[ckd_combined['Color'] == 'gold']\n",
"\n",
"fig, ax = plt.subplots(figsize=(4,4))\n",
"\n",
"ax.scatter(glucose_color_darkblue['White Blood Cell Count'], \n",
" glucose_color_darkblue['Glucose'], \n",
" label='Color=darkblue', \n",
" color='darkblue', s=10)\n",
"\n",
"ax.scatter(glucose_color_gold['White Blood Cell Count'], \n",
" glucose_color_gold['Glucose'], \n",
" label='Color=gold', \n",
" color='gold', s=10)\n",
"\n",
"x_label = 'White Blood Cell Count'\n",
"y_label = 'Glucose'\n",
"y_vals = ax.get_yticks()\n",
"plt.ylabel(y_label)\n",
"ax.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
"plt.xlabel(x_label)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b0d66611",
"metadata": {},
"source": [
"As you can see, non-CKD individuals are all clustered in the lower-left. Most of the patients with CKD are above or to the right of that cluster... but not all. There are some patients with CKD who are in the lower left of the above figure (as indicated by the handful of blue dots scattered among the gold cluster). What this means is that you can't tell for certain whether someone has CKD from just these two blood test measurements.\n",
"\n",
"If we are given Alice's glucose level and white blood cell count, can we predict whether she has CKD? Yes, we can make a prediction, but we shouldn't expect it to be 100% accurate. **Intuitively**, it seems like there's a natural strategy for predicting: plot where Alice lands in the scatter plot; if she is in the lower-left, predict that she doesn't have CKD, otherwise predict she has CKD. \n",
"\n",
"This isn't perfect -- our predictions will sometimes be wrong. (Take a minute and think it through: for which patients will it make a mistake?) As the scatterplot above indicates, sometimes people with CKD have glucose and white blood cell levels that look identical to those of someone without CKD, so any classifier is inevitably going to make the wrong prediction for them.\n",
"\n",
"Can we automate this on a computer? Well, the **nearest neighbor classifier** would be a reasonable choice here too. Take a minute and think it through: how will its predictions compare to those from the intuitive strategy above? When will they differ?\n",
"\n",
"Its predictions will be pretty similar to our intuitive strategy, but occasionally it will make a different prediction. In particular, if Alice's blood test results happen to put her right near one of the blue dots in the lower-left, the intuitive strategy would predict 'not CKD', whereas the nearest neighbor classifier will predict 'CKD'.\n",
"\n",
"There is a simple generalization of the nearest neighbor classifier that fixes this anomaly. It is called the **k-nearest neighbor classifier**. To predict Alice's diagnosis, rather than looking at just the one neighbor closest to her, we can look at the 3 points that are closest to her, and use the diagnosis for each of those 3 points to predict Alice's diagnosis. In particular, we'll use the majority value among those 3 diagnoses as our prediction for Alice's diagnosis. Of course, there's nothing special about the number 3: we could use 4, or 5, or more. (It's often convenient to pick an odd number, so that we don't have to deal with ties.) In general, we pick a number $k$, and our predicted diagnosis for Alice is based on the $k$ patients in the training set who are closest to Alice. Intuitively, these are the $k$ patients whose blood test results were most similar to Alice, so it seems reasonable to use their diagnoses to predict Alice's diagnosis.\n",
"\n",
"The $k$-nearest neighbor classifier will now behave just like our intuitive strategy above."
]
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"\n",
"## Section Practice\n",
"\n",
"Use this short exercise to check the main workflow from this section.\n"
]
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"### Exercise: Practice Nearest Neighbors\n",
"# A subscription service wants to predict whether a new customer will renew.\n",
"# Each past customer has two attributes in standard units and a known label.\n",
"# 1. Compute the distance from new_customer to each past customer.\n",
"# 2. Predict with the single nearest neighbor (1-NN).\n",
"# 3. Predict by majority vote of the 3 nearest neighbors (3-NN).\n",
"# Print exactly:\n",
"# Nearest neighbor: C\n",
"# 1-NN prediction: churn\n",
"# 3-NN prediction: renew\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"customers = pd.DataFrame({\n",
" 'name': ['A', 'B', 'C', 'D', 'E'],\n",
" 'visits': [1.2, 0.8, -0.1, -1.0, 0.3],\n",
" 'spend': [0.9, 1.1, 0.4, -1.2, 0.1],\n",
" 'label': ['renew', 'renew', 'churn', 'churn', 'renew'],\n",
"})\n",
"new_customer = np.array([0.2, 0.5])\n",
"### Your code starts here\n",
"\n",
"\n",
"\n",
"\n",
"### Your code ends here\n"
]
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"text": [
"Nearest neighbor: C\n",
"1-NN prediction: churn\n",
"3-NN prediction: renew\n"
]
}
],
"source": [
"### Solution\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"customers = pd.DataFrame({\n",
" 'name': ['A', 'B', 'C', 'D', 'E'],\n",
" 'visits': [1.2, 0.8, -0.1, -1.0, 0.3],\n",
" 'spend': [0.9, 1.1, 0.4, -1.2, 0.1],\n",
" 'label': ['renew', 'renew', 'churn', 'churn', 'renew'],\n",
"})\n",
"new_customer = np.array([0.2, 0.5])\n",
"\n",
"attributes = customers[['visits', 'spend']]\n",
"customers['distance'] = np.sqrt(((attributes - new_customer) ** 2).sum(axis=1))\n",
"nearest = customers.sort_values('distance')\n",
"\n",
"print('Nearest neighbor:', nearest['name'].iloc[0])\n",
"print('1-NN prediction:', nearest['label'].iloc[0])\n",
"print('3-NN prediction:', nearest['label'].head(3).value_counts().idxmax())\n"
]
}
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