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The KNN Algorithm – Explanation, Opportunities, Limitations

7 min
11th August, 2023

K Nearest Neighbor (KNN) is a very simple, easy-to-understand, and versatile machine learning algorithm. It’s used in many different areas, such as handwriting detection, image recognition, and video recognition. KNN is most useful when labeled data is too expensive or impossible to obtain, and it can achieve high accuracy in a wide variety of prediction-type problems.

KNN is a simple algorithm, based on the local minimum of the target function which is used to learn an unknown function of desired precision and accuracy. The algorithm also finds the neighborhood of an unknown input, its range or distance from it, and other parameters. It’s based on the principle of “information gain”—the algorithm finds out which is most suitable to predict an unknown value. 

In this article, we’re going to explore key concepts behind the KNN algorithm and analyze a real-world KNN use case. 

The lazy learning paradigm and KNN algorithm

KNN is widely known as an ML algorithm that doesn’t need any training on data. This is much different from eager learning approaches that rely on a training dataset to perform predictions on unseen data. With KNN, you don’t need a training phase at all.

KNN relies on observable data similarities and sophisticated distance metrics to generate accurate predictions. This technique may seem a bit counterintuitive and not trustworthy at first, but it’s actually very reliable. It’s popular in many fields, including:

  • Computer Vision: KNN performs classification tasks. It handles image data well, and it’s considered a fine option for classifying a bunch of diverse images based on similarities.
  • Content Recommendation: KNN is great for content recommendation. It’s used in many recommendation system engines and continues to be relevant even though there are newer, more powerful systems already available.

Read also

Best MLOps Tools for Your Computer Vision Project Pipeline

Curse of dimensionality

The curse of dimensionality means that KNN performs best with a low number of features. When the number of features increases, then it requires more data. When there’s more data, it creates an overfitting problem because no one knows which piece of noise will contribute to the model. KNN performs better with low dimensionality (as demonstrated by a study by Gu and Shao in 2014). 

KNN inner workings

Surprisingly enough, the KNN algorithm is quite accessible and easy to understand. For an observation that’s not in the dataset, the algorithm will simply look for the K number of instances defined as similar based on the closest perimeter to that observation. Any data point falls under a specific group if it’s close enough to it.  

For K neighbors, the algorithm will use their output to calculate the variable y of the observation that we want to predict.

As such: 

  • If KNN is used for regression tasks, the predictions will be based on the mean or median of the K closest observations.
  • If KNN is used for classification purposes, the mode of the closest observations will serve for prediction.

A close look at the structure of KNN

Suppose we have:

  • a dataset D
  • a defined distance metric that we’ll be using to measure the distance between the set of observations, 
  • and an integer K representing the minimum number of near neighbors we should consider to establish proximity. 

In order to predict the output y for a new observation X, will follow these steps:

  1. Calculate the total distances between the X observable and all the data points.
  2. Retain the K observations that constitute the smaller distances to the observable point X.
  3. With the y outputs taken from the K observations:
    1. apply the mean of the y deductions if it’s a regression problem,
    2. use the mode of y deductions if it’s a classification problem.
  4. The final prediction will be the value calculated in step 3.
  5. A detailed version of the algorithm can be found in pseudo-code:
KNN pseudo code

How distances and similarities are carried out in KNN

At its heart, KNN uses different sorts of distance metrics to evaluate the proximity of two data points (their similarity). A core assumption of KNN is:

The closer two given points are to each other, the more related and similar they are. 

Several distance metrics determine correlation and similarity. Even though there are plenty of distance functions to choose from, we should always use the functions that best fit the nature of our data. Notable metrics include:

Distance Metric Purpose
Euclidean Distance Mostly used for quantitative data
Taxicab Geometry Used when the data types are heterogenous
Minkowski distance Intended for real-valued vector spaces
Jaccard index Often used in applications when dealing with binarized data
Hamming distance Typically used with data transmitted over computer networks. And also used with categorical variables.

Note: I highly encourage you to look up this article about the effects of distance measure choices when using KNN for classification tasks.

Most ML libraries offer these metrics out of the box. So, you don’t need to code them from scratch, but you might want to do it just to understand how they work.

Choose the K value

To select the value of K that fits your data, we run the KNN algorithm multiple times with different K values. We’ll use accuracy as the metric for evaluating K performance. If the value of accuracy changes proportionally to the change in K, then it’s a good candidate for our K value.

When it comes to choosing the best value for K, we must keep in mind the number of features and sample size per group. The more features and groups in our data set, the larger a selection we need to make in order to find an appropriate value ​​of K.

When we decrease the value of K to 1, our predictions become less stable. The accuracy decreases and the metric “F-Measure” becomes more sensitive to outliers. For better results, increase the value of K until the F-Measure value is higher than the threshold.

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Also, you shouldn’t forget to take into account the effect of the K value on the sample class distribution. If you tend to have many people in one group, then you should increase K. Conversely if your data set often has a significant number of people in one group, you need to decrease K.

Here are some examples of varying the value of K for a specific dataset:

As you can see, the more neighbors you use, the more accurate the segmentation. However, as we increase the K value until reaching N (the total number of data points), we seriously risk overfitting our model, leaving it unable to generalize well on unseen observations.

A practical use case of the KNN algorithm

To illustrate what we’ve been explaining so far, we’ll try to use KNN against a well-known dataset recording the symptoms of breast cancer of clinical patients from Wisconsin in the US.

First, let’s download the dataset from UCI Machine Learning Repository. You’ll find the data folder with a detailed explanation of each attribute and the target variable we’ll try to predict. 

Set up the project

Download the dataset and install all required packages:

pip install scikit-learn
pip install matplotlib
pip install pandas

Import the dataset and read it as csv:

import pandas as pd

data = pd.read_csv('')
KNN dataset description
Description of the dataset | Source: Author

Add the dataset column names:

data.columns = ['Id', 'Clump Thickness', 'Unifomrmity of Cell size', 'Unifomrmity of Cell shape', 'Marginal Adhesion',
                'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']

Visualize the data using the Plotly library

The dataset is clearly unbalanced and unevenly distributed. If we plot the two groups of the target variable, the Benign group records largely more cases than the Malignant one. That can be explained and correlated to the fact that some events are less likely to happen than others. 

Here is a plot comparing the balance between Benign and Malignant records:

import matplotlib.pyplot as plt
import chart_studio.plotly as py
import plotly.graph_objects as go
import plotly.offline as pyoff

target_balance = data['Class'].value_counts().reset_index()

target_class = go.Bar(
    name = 'Target Balance',
    x = ['2-Benign, '4-Malignant'],
    y = target_balance['Class']

fig = go.Figure(target_class)
KNN Begning-Malignant classes
Beginning and Malignant Group Classes | Credit: Author

Another insightful statistic to show is the Mitosis level in clinical patients within both groups. Level 1 is the lowest and 9 the highest. The Mitosis level is an important factor that causes tumors to grow and evolve. Naturally, the malignant group will register a higher number of patients suffering from advanced Mitosis stages.

# Record of Mitosis in the Beginning and Malignant Group 
beg_class_pat = data.loc[data['Class'] == 2]
mal_class_pat = data.loc[data['Class'] == 4]

Mith_10_beg = beg_class_pat['Mitoses'].value_counts().reset_index()
Mith_10_mal = mal_class_pat['Mitoses'].value_counts().reset_index()
# Grouping both results:
fig = go.Figure(data=[
    go.Bar(name='Levels of Mitoses in Begnin Group', x=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'],
    go.Bar(name='Levels of Mitoses in Malignant Group', x=['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'],
KNN level of mitoses
Level of Mitosis in Both clinical Groups | Credit: Author

Initialize your Neptune experiment

I usually like to start by creating a virtual environment where I’ll be installing and required packages for the project.

conda create --name neptune python=3.6
  • Then, install the Neptune client library with all its dependencies. Check it out here: Neptune Documentation.
  • Start with your experiment. Set up the required parameters we’ll be working with:
run = neptune.init_run(

run["Algorithm"] = "KNN"

params = {
    "algorithm": auto,
    "leaf_size": 30,
    "metric": minkowski,
    "metric_params": None,
    "N_jobs": None,
    "N_neighbors": None,
    "P": 2
    "weight": uniform
run["parameters"] = params
  • Before starting with the KNN model, consider preprocessing the data. All attributes are of type int64 and there’s no null value. We also need to segregate the data into training and testing splits.
# Preprocessing
data = data.loc[data['Bare Nuclei'] != '?']
data['Bare Nuclei'] = data['Bare Nuclei'].astype('int64')
# Segregating features and targets separately
features = data.loc[:, data.columns != 'Class']
features = features.loc[:, features.columns != 'Id']
target = data['Class']
# Splitting the data
x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=123) 

Training the model

Choosing the best K value

We’ll iterate through a range of three different K values and try to see which K will best fit our case and data. First, let’s try to understand what exactly does K influences the algorithm. If we see the last example, given that all the 6 training observations remain constant, with a given K value we can make boundaries of each class. Now, that’s a nice and useful property of K for the algorithm to use. But, as you may know, the value of K isn’t static. The value of K changes with each successive iteration. This means that we’ll have a different set of boundary values for each class the second time around. 


See how to keep track of your model training in different frameworks.

We’ll be logging each K iteration in Neptune. Check the documentation to see how to do it.

KNN log value in Neptune
KNN log value in Neptune | Credit: Author

We observe that the maximum value reached is 0.992 and it appears for K = 6. Other values for K = {2, 4, 5} are 0.98. Since we have more than 3 candidates sharing the same value, we can conclude that the optimal K value is 5. 

In this particular case, we’re using the Minkowski distance for the KNN model. But it could be the case that if you try different distances, you could obtain other K values. 

KNN Classifier appears as follow:

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform')

Once we have decided that the best value K is 5, we’ll proceed to train the model with the data and check its overall accuracy score.

knn = KNeighborsClassifier(n_neighbors=5), y_train)
predictions = knn.predict(x_test)
metrics.accuracy_score(y_test, predictions)
KNN accuracy score
Final accuracy score | Credit: Author

KNN limitations

KNN is a fairly simple algorithm to understand. It doesn’t rely on any ML model that works inside and makes predictions. KNN is a classification algorithm that only needs to know the number of categories (one or more). This means it can easily determine if a new category should be added without any data on how many other categories there may be. 

The downside to this simplicity is that it doesn’t make predictions for rare things (like new diseases), where KNN can’t predict because it has no idea what the prevalence of a rare thing would be in an otherwise healthy population.

Although KNN produces good accuracy on the testing set, the classifier remains slower and costlier in terms of time and memory. It requires large memory for storing the entire training dataset for prediction. Furthermore, Euclidean distance is very sensitive to magnitudes, hence features in the dataset that has high magnitudes will always weigh more than their counterparts with low magnitudes.

Finally, we should keep in mind that KNN isn’t suited for large-dimensional datasets with all we’ve mentioned above. 


Hopefully, you now know a bit more about how the KNN algorithm works. We’ve explored many concepts about how KNN stores the entire dataset to make predictions.

KNN is one of the many lazy learning algorithms that don’t base predictions on a learning model. KNN makes predictions on the fly (just in time) by averaging the similarity between an input observation and the data already present. 

I’ll leave you with some useful resources to expand your understanding of KNN even more:

Thank you for reading!

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