StatQuest: K-nearest neighbors, Clearly Explained

Summary

In this Stack Quest episode, the host introduces the K-nearest neighbors algorithm, a straightforward method for classifying data. Using a dataset of known cell types from an intestinal tumor, the process involves clustering the data, adding an unknown cell, and classifying it based on its proximity to known neighbors. The choice of 'K' is crucial, with smaller values being sensitive to outliers and larger values smoothing results but potentially diluting minority categories. The episode also touches on machine learning terminology and offers insights on selecting an optimal 'K' value through trial and validation.

Takeaways

• 📚 The script introduces the K nearest neighbors algorithm, a method used for classifying data.
• 🔍 It starts with a dataset of known categories, using an example of different cell types from an intestinal tumor.
• 📈 The data is clustered, in this case, using PCA (Principal Component Analysis).
• 📊 A new cell with an unknown category is added to the plot for classification.
• 🔑 The classification of the new cell is based on its nearest neighbors among the known categories.
• 👉 If K=1, the nearest neighbor's category defines the new cell's category.
• 📝 For K>1, the category with the majority vote among the K nearest neighbors is assigned to the new cell.
• 🎯 The script demonstrates the algorithm with examples, including scenarios where the new cell is between categories.
• 🗳️ The concept of 'voting' is used to decide the category when the new cell is close to multiple categories.
• 🌡️ The script also explains the application of the algorithm to heat maps, using hierarchical clustering as an example.
• 🔢 The choice of K is subjective and may require testing different values to find the most effective one.
• 🔧 Low values of K can be sensitive to noise and outliers, while high values may smooth over details but risk overshadowing smaller categories.

Q & A

What is the main topic discussed in the Stack Quest video?

The main topic discussed in the Stack Quest video is the K nearest neighbors (KNN) algorithm, a method used for classifying data.

What is the purpose of using the K nearest neighbors algorithm?

The purpose of using the K nearest neighbors algorithm is to classify new data points based on their similarity to known categories in a dataset.

What is the first step in using the KNN algorithm as described in the video?

The first step is to start with a dataset that has known categories, such as different cell types from an intestinal tumor.

How is the data prepared before adding a new cell with an unknown category in the KNN algorithm?

The data is clustered, for example using PCA (Principal Component Analysis), to reduce dimensions and visualize the data effectively.

What is the process of classifying a new cell with an unknown category in the KNN algorithm?

The new cell is classified by looking at the nearest annotated cells, or nearest neighbors, and assigning the category that gets the most votes among these neighbors.

What does 'K' represent in the K nearest neighbors algorithm?

In the K nearest neighbors algorithm, 'K' represents the number of closest neighbors to consider when classifying a new data point.

How does the KNN algorithm handle a situation where the new cell is between two or more categories?

If the new cell is between two or more categories, the algorithm takes a vote among the K nearest neighbors, and assigns the category that gets the most votes.

What is a potential issue with using a very low value for K in the KNN algorithm?

Using a very low value for K, like 1 or 2, can be noisy and subject to the effects of outliers, potentially leading to inaccurate classifications.

What is a potential issue with using a very high value for K in the KNN algorithm?

Using a very high value for K can smooth over the data too much, potentially causing a category with only a few samples to always be outvoted by larger categories.

What is the term used for the data with known categories used in the initial clustering in machine learning?

The data with known categories used in the initial clustering is called 'training data' in machine learning.

How can one determine the best value for K in the KNN algorithm?

There is no physical or biological way to determine the best value for K; one may have to try out a few values and assess how well the new categories match the known ones by pretending part of the training data is unknown.

Outlines

00:00

🤖 Introduction to K-Nearest Neighbors Algorithm

In this introductory segment, the video script discusses the K-nearest neighbors (KNN) algorithm, a method used for classifying data. The script begins with a brief introduction to Stack Quest, a series presented by the genetics department at the University of North Carolina at Chapel Hill. The main focus is on the KNN algorithm, which is described as a simple way to classify data based on its similarity to known categories. The script outlines a hypothetical scenario involving the classification of cell types from an intestinal tumor, using Principal Component Analysis (PCA) for data clustering. It then explains the process of classifying a new, unknown cell by considering its proximity to the nearest annotated cells, with the classification decision depending on the majority vote of these 'nearest neighbors'. The script also touches on the concept of 'K' in KNN, emphasizing that the value of K can significantly influence the outcome and suggesting that it might require experimentation to find the optimal value. The segment concludes with a brief mention of machine learning and data mining terminology, specifically defining 'training data' as the data set with known categories used for initial clustering.

05:01

📢 Conclusion and Call to Action

The concluding paragraph of the video script invites viewers to subscribe to the channel for more content like the one they just watched. It encourages viewers to share their ideas for future Stack Quest topics in the comments section. The script maintains an engaging tone, expressing enthusiasm for the subject matter and inviting viewer participation. It wraps up with a reminder to tune in for the next episode, creating anticipation for future content.

Keywords

💡K nearest neighbors algorithm

The K nearest neighbors algorithm is a simple machine learning technique used for classification. It works by finding the 'K' closest data points to a new, unknown data point and assigning the majority class among these neighbors as the classification for the new point. In the video, this algorithm is used to classify a new cell type based on its proximity to known cell types, illustrating its practical application in data classification.

💡Data classification

Data classification is the process of organizing data into different categories based on certain characteristics or features. In the context of the video, the K nearest neighbors algorithm is employed to classify a new cell based on its similarity to known cell types, demonstrating the concept of using data classification for decision-making in biological research.

💡PCA (Principal Component Analysis)

PCA is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variance as possible. In the script, PCA is mentioned as the method used to cluster the data, which helps in visualizing the different cell types and their relationships in a lower-dimensional space, facilitating the classification process.

💡Clustering

Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. The video script describes using PCA for clustering different cell types from an intestinal tumor, which is a way to prepare the data for the K nearest neighbors algorithm to classify new, unknown cells.

💡Training data

Training data refers to the dataset used to teach a machine learning model to make predictions or decisions. In the video, the known categories of cells from an intestinal tumor serve as the training data, which the K nearest neighbors algorithm uses to learn how to classify new, unknown cells.

💡Outliers

Outliers are data points that are significantly different from other observations, potentially skewing the results of an analysis. The script mentions that low values for 'K' can be subject to the effects of outliers, which means that the classification of a new data point could be influenced by distant, atypical points rather than the nearest, most relevant ones.

💡Hierarchical clustering

Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. The video script uses a heatmap drawn with hierarchical clustering to illustrate how the K nearest neighbors algorithm can be applied in a different context, showing the classification of an unknown cell based on its proximity to clusters of known cells.

💡Heatmap

A heatmap is a graphical representation of data where individual values are represented as colors. In the video, a heatmap is used to visualize the results of hierarchical clustering, providing a visual tool to understand the distribution and relationships between different cell types, which is crucial for the classification of a new cell.

💡Machine learning

Machine learning is a subset of artificial intelligence that provides systems the ability to learn from and make decisions based on data. The video script introduces the K nearest neighbors algorithm as a simple example of machine learning, showing how it can be used to classify data without being explicitly programmed to do so.

💡K value

The 'K' value in the K nearest neighbors algorithm determines the number of nearest neighbors to consider when classifying a new data point. The script discusses the importance of choosing an appropriate 'K' value, as it can affect the algorithm's sensitivity to outliers and its ability to accurately classify new data points.

💡Data mining

Data mining is the process of discovering patterns in large datasets. The video script touches on data mining terminology, such as 'training data,' and uses the K nearest neighbors algorithm as an example of how data mining techniques can be applied to classify and understand complex biological data.

Highlights

Introduction to the K nearest neighbors algorithm for classifying data.

Using the algorithm with known cell types from an intestinal tumor dataset.

Step 1: Starting with a dataset with known categories and clustering using PCA.

Step 2: Adding a new cell with an unknown category to the plot.

Classifying the new cell by finding the nearest annotated cells.

If K=1, the nearest neighbor defines the category of the unknown cell.

If K=11, the majority vote among the 11 nearest neighbors determines the category.

Example of a new cell being halfway between two categories.

The principle of majority vote for classification when the new cell is between categories.

Applying the same principle to heat maps created with hierarchical clustering.

If K is odd, ties can be avoided in the majority vote.

If a tie occurs, a coin flip or no category assignment is considered.

Discussion on machine learning and data mining terminology, including 'training data'.

Importance of selecting the right value for K in the K nearest neighbors algorithm.

Low values for K can be noisy and subject to outliers.

High values for K may cause minority categories to be outvoted.

Encouragement to subscribe for more educational content like Stack Quest.

Invitation for viewers to suggest topics for future Stack Quest videos.

Transcripts

[Music]

St

Quest St

Quest stack

Quest hello and welcome to stack Quest

stack Quest is brought to you by the

friendly folks in the genetics

department at the University of North

Carolina at Chapel

Hill today we're going to be talking

about the K nearest neighbors algorithm

which is a super simple way to classify

data

in a nutshell if you already had a lot

of data that Define these cell types we

could use it to decide which type of

cell this guy is let's see it in

action step one start with a data set

with known categories in this case we

have different cell types from an

intestinal

tumor we then cluster that data in this

case we used PCA

step two add a new cell with unknown

category to the plot we don't know this

cell's category because it was taken

from another tumor where the cells were

not properly sorted and so what we want

to do is we want to classify this new

cell we want to figure out what cell

it's most similar to and then we're

going to call it that type of

cell step three we classify the new cell

by looking at the nearest nearest

annotated cells I.E the nearest

neighbors if the K in K nearest

neighbors is equal to one then we will

only use the nearest neighbor to define

the category in this case the category

is green because the nearest neighbor is

already known to be the green cell type

if k equals 11 we would use the 11

nearest Neighbors in this case the

category is still green because the 11

cells that are closest to the unknown

cell are already

green now the new cell is somewhere more

interesting it's about halfway between

the green and the red cells if k equals

11 and the new cells between two or more

categories we simply pick the category

that gets the most

votes in this case seven nearest

neighbors are

red three nearest neighbors are

orange one nearest neighbor is

green since red got the most votes the

final assignment is

red this same principle applies to heat

Maps this heat map was drawn with the

same data and clustered using

hierarchical

clustering if our new cell ended up in

the middle of the light blue cluster and

if k equals 1 we just look at the

nearest cell and that cell is light blue

so we classify the unknown cell as a

light blue cell if k equals 5 we'd look

at the five nearest cells which are also

light blue so we'd still classify the

unknown cell as light blue if the new

cell ended up closer to the edge of the

light blue cells and k equals 11 then we

take a vote

seven nearest neighbors are light blue

and four are light green so we'd still

go with light blue if the new cell is

right between two

categories well if K is odd then we can

avoid a lot of

ties if we still get a tied vote we can

flip a coin or decide not to assign the

cell to a

category before we go let's talk about a

little machine learning SL data mining

terminology the data used for the

initial clustering the data where we

know the categories in advance is called

training data

bam a few thoughts on picking a value

for K there is no physical or biological

way to determine the best value for K so

you may have to try out a few values

before settling on one do this by

pretending part of the training data is

unknown and then what you do is you

categorize that unknown data using the K

nearest neighbor algorithm and you

assess how good the new categories match

what you know

already low values for K like k equal 1

or k equals 2 can be noisy and subject

to the effects of

outliers large values for K smooth over

things but you don't want K to be so

large that a c category with only a few

samples in it will always be outvoted by

other

categories hooray we've made it to the

end of another exciting stack Quest if

you like this stack Quest go ahead and

subscribe to my channel and you'll see

more like it and if you have any ideas

of things you'd like me to do a stack

Quest on feel free to put those ideas in

the comments okay guess that's it tune

in next time for another exciting stag

Quest