11 key concepts of Machine Learning.

— Supervised Learning Edition —

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Before starting, remember that, if you follow me, one of your enemies will be immediately destroyed (and you'll get to read more of these threads, of course.)

And if you don't follow me, well, you just hurt my feelings.

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1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

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2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

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3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

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4. Labeled sample

Labeled samples are used to train and validate the model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding label.

For example, a labeled sample could be:

([3, 2, 1500], 350000)
5. Unlabeled sample

Unlabeled samples contain features, but they don't contain the label: (x, ?)

We usually use a model to predict the labels of unlabeled samples.

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6. Model

A model defines the relationship between features and the label.

You can think of a model as a set of rules that, given certain features, determines the corresponding label.

For example, given the # of bedrooms, bathrooms, and square footage, we get the price.

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7. Training

Training is a process that builds a model.

We show the model labeled samples during training and allow the model to gradually learn the relationships between features and the label.

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8. Validation

Validation is the process that lets us know whether a model is any good.

Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the labels.

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9. Inference

Inference is the process of applying a trained model to unlabeled samples to obtain the corresponding labels.

In other words, "inference" is the process of making predictions using a model.

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10. Regression

A regression model predicts continuous values, for example:

- the value of a house
- the price of a stock
- tomorrow's temperature

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11. Classification

A classification model predicts discrete values, for example:

- the picture is showing a dog or a cat
- the message is spam or not
- the forecast is sunny or overcast

More from Santiago

More from Machine learning

Happy 2⃣0⃣2⃣1⃣ to all.🎇

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.
⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city
https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel
https://t.co/f1joTRaG55

✔️ @dollarsanddata
https://t.co/Mj1owkzRc8

✔️ @awealthofcs
https://t.co/y81KHfh8cn

✔️ @iancassel
https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery
https://t.co/VsNwRStY9C

✔️ @bgurley
https://t.co/NKXGtaB6HQ

✔️ @CBinsights
https://t.co/H77hNp2X5R

✔️ @benedictevans
https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting
https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth
https://t.co/kcLKITRLz1

✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital
https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas
Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method
https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

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