This section introduces the idea of “similarity”.
Why?:
 Simplicity
 Many business tasks require a measure of “similarity”
 Works well
Business reasoning
Why would businesses want to use a measure of similarity? What business problems map well to similarity classifiers?
 Find similar companies on a CRM
 Find similar people in an online dating app
 Find similar configurations of machines in a data centre
 Find pictures of cats that look like this cat
 Recommend products to buy from similar customers
 Find similar wines
Similarity
What is similarity?

We can say that two wines are similar if they have the same colour, alcohol content, tastes, etc.

We can say that configurations of machines are similar if they have the same RAM, CPU, HDD, etc.
In other words, we are comparing the features of the observation.
Observations are similar if they have matching features.
Distance
What is the best way of reducing the similarity into a single distance measurement?
The simplest conversion would be to use the Euclidean distance (a.k.a. L2 norm, Pythagoras' Theorem):
$$ d_{Euclidean}(\mathbf{x}, \mathbf{y}) = \mathbf{x}  \mathbf{y}=\sqrt{(x_1  y_1)^2 + (x_2  y_2)^2 + …} $$
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I.e. small distances are very similar, large distances are dissimilar.
Note that although we use the word distance, this measurement has no units. We’re potentially comparing multiple types of features, so a real “distance” doesn’t make sense.
Nearest Neighbour Algorithm
 Calculate distance to all observations
 Find the next closest observation
 Recommendations: List the next nearest
 Classification: Predict the same class as the nearest observations
 Regression: Predict the same value as the nearest observations
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Now we have a measure of distance, we can perform the nearest neighbour algorithm!
If we wanted to find the next similar wine for example, we’d simply calculate the distance between the current wine and all other wines.
Our next wine would be the one with the smallest distance!
If we wanted to perform classification, then we’d do the same but predict a classification using the nearest neighbour’s class.
If we wanted to perform regression, then we’d do the same but pick the same value as the closest neighbour.
Simple!
class: puretable, puretablestriped
Example: Whiskey recommendations
Distillery  Body  Sweetness  Smoky  Medicinal 

Aberfeldy  2  2  2  0 
Aberlour  3  3  1  0 
AnCnoc  1  3  2  0 
Ardbeg  4  1  4  4 
Ardmore  2  2  2  0 
Columns: ['Distillery', 'Body', 'Sweetness', 'Smoky', 'Medicinal', 'Tobacco', 'Honey', 'Spicy', 'Winey', 'Nutty', 'Malty', 'Fruity', 'Floral', 'Postcode', ' Latitude', ' Longitude']
???
Do you like whiskey? (Say yes!)
If we had a dataset detailing whiskey characteristics, then we could take your favourite whiskey and return the most similar whiskeys as a personalised recommendation!
Algorithm
given favourite whiskey
foreach whiskey:
dist = 0
foreach feature:
dist += (favorite[feature]  whiskey[feature])^2
neighbours[whiskey] = dist
sort(neighbours by value)
print(first 5 neighbours)
Results
So, let’s go for a supersmoke: Laphroig. The results:
[
(array([4, 2, 4, 4, 1, 0, 0, 1, 1, 1, 0, 0, 'Laphroig'], dtype=object), 0.0),
(array([4, 1, 4, 4, 1, 0, 1, 2, 1, 1, 1, 0, 'Lagavulin'], dtype=object), 2.0),
(array([4, 1, 4, 4, 0, 0, 2, 0, 1, 2, 1, 0, 'Ardbeg'], dtype=object), 3.0),
(array([3, 2, 3, 3, 1, 0, 2, 0, 1, 1, 2, 0, 'Clynelish'], dtype=object), 3.4641016151377544),
(array([3, 1, 4, 2, 1, 0, 2, 0, 2, 1, 1, 1, 'Caol Ila'], dtype=object), 3.7416573867739413)
]
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We can see that Laphroig matches itself perfectly, as it should.
Next up is Lagavulin and Ardbeg.
According to the smoke classifications (the third column), and others, these are pretty good recommendations.
However, this points out how important your data is. I’ve definitely have nonsmokey Ardbeg’s before.