StatQuest: K-nearest neighbors, Clearly Explained
Summary
TLDRIn 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
🤖 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.
📢 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.
Mindmap
Keywords
💡K nearest neighbors algorithm
💡Data classification
💡PCA (Principal Component Analysis)
💡Clustering
💡Training data
💡Outliers
💡Hierarchical clustering
💡Heatmap
💡Machine learning
💡K value
💡Data mining
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
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