Gentle Introduction to Machine Learning: Predictions and Testing Data
This StatQuest video, originally presented at the Society for Scientific Advancement conference, demystifies machine learning using simple, fun examples. The core message is that machine learning is fundamentally about making predictions and classifications, and the true test of any model lies in how it handles new, unseen data.
What is Machine Learning?
Machine learning is all about making predictions and classifications. The video uses two silly examples to illustrate this core concept:
Example 1: The Silly Song Decision Tree
A decision tree is a simple machine learning method used to classify people into categories (e.g., "loves StatQuest" or "doesn't love StatQuest"). The tree asks a series of questions:
- Do you like silly songs?
- If yes: Are you interested in machine learning?
- If yes: Predict "loves StatQuest."
- If no: Are you interested in statistics?
- If yes: Predict "loves StatQuest."
- If no: Predict "doesn't love StatQuest."
- If no: Are you interested in machine learning?
- If yes: Predict "loves StatQuest."
- If no: Are you interested in statistics?
- If yes: Predict "loves StatQuest."
- If no: Predict "doesn't love StatQuest."
- If yes: Are you interested in machine learning?
This tree illustrates how a machine learning model can classify new individuals based on their answers. For a deeper look into different algorithms, check out the guide on Machine Learning Types Explained: Supervised vs Unsupervised vs Reinforcement.
Example 2: The Yam Speed Prediction
This example uses a simple dataset to predict how fast someone can run 100 meters based on how much yam they eat. The trend (a "black line") is fitted to the data to make predictions. The more yam eaten, the faster the predicted speed.
The Two Most Important Concepts in Machine Learning
The video highlights the critical distinction between Training Data and Testing Data.
1. Training Data vs. Testing Data
- Training Data: The original data used to build or "fit" a machine learning model.
- Testing Data: New, unseen data used to evaluate the model's performance.
2. The Bias-Variance Tradeoff (Don't Be Fooled!)
A model that fits the training data perfectly (like a "green squiggle") might not make good predictions on new data. The video demonstrates this by comparing two models:
- Black Line (Simple Model): Fits the training data only moderately well but makes excellent predictions on the testing data (small total prediction error).
- Green Squiggle (Complex Model): Fits the training data perfectly but makes poor predictions on the testing data (large total prediction error).
Key Insight: The best model is the one that performs best on the testing data, not the one that fits the training data best. This is the essence of the bias-variance tradeoff, which is explored in depth in the Introduction to Linear Predictors and Stochastic Gradient Descent summary.
How to Evaluate a Machine Learning Method
The process for selecting the best model is straightforward:
- Build the model using the training data.
- Test the model using the held-out testing data.
- Calculate the error by measuring the distance between the actual values (truth) and the predicted values from the model.
- Compare models based on their total error on the testing data. The model with the lowest error is the winner.
How to Split Data (A Quick Note)
The video acknowledges that deciding which data goes into the training set and which into the testing set is critical. While the example used an arbitrary split, there are formal methods for doing this, such as cross-validation (which is covered in other StatQuest videos). For a complete foundational understanding, consider the Statistics for Data Science: The Complete Beginner's Guide.
Summary
- Machine learning is about making predictions and classifications.
- The method's sophistication doesn't matter as much as its performance on testing data.
- The bias-variance tradeoff warns against over-engineering a model to perfectly fit training data at the expense of generalization.
- The best model is the one that has the smallest prediction error on new, unseen data.
If you’re just starting out with this topic, the 100 Days of Machine Learning: Comprehensive Beginner to Intermediate Guide offers a structured path to build on these concepts. For a more extensive hands-on experience, the Complete Machine Learning Course: 60-Hour Hands-On Tutorial with Python Projects is an excellent next step.
Note: This content is based on the StatQuest introduction. The output has been enhanced with relevant internal links for further learning.
gonna start this tech quest with silly song but if you don't like silly songs that's okay
stack quests hello I'm Josh stormer and welcome to stack quest today we're going to do a gentle introduction to machine
learning note this stack quest was originally prepared for and presented at the Society for scientific advancements
annual conference one of the things that Sosa does is promote science and technology in Jamaica let's start with a
silly example do you like silly songs if you like silly songs are you interested in machine learning if you like silly
songs and machine learning then you'll love stack quest if you like silly songs but not machine
learning are you interested in statistics if you like silly songs and statistics but not machine learning then
you'll still love stack quest otherwise you might not like stack quest won't Wang if you don't like silly songs are
you interested in machine learning if you don't like silly songs but you like machine learning then you'll love stack
quest if you don't like silly songs or machine learning are you interested in statistics if you don't like silly songs
or machine learning but you're interested in statistics then you will love stack quest otherwise you might not
like stack quest wah wah this is a silly example but it illustrates a decision tree a simple machine learning method
the purpose of this particular decision tree is to predict whether or not someone will love stack quest
alternatively we could say that this decision tree classifies a person as either someone who loves stack quest or
someone who doesn't since decision trees are a type of machine learning then if you understand how we use this tree to
predict or classify if someone would love stack quest you are well on your way to understanding machine learning
BAM here's another silly example of machine learning
imagine we measured how quickly someone could run 100 meters and how much yam they ate this is me I'm not very fast
and I don't eat much yam these are some other people and this is Shane bolt hold is very fast
Andy eats a lot of yam given this pretend data we see that the more yam someone eats the faster they run the
100-meter dash we can fit a black line to the data to show the trend but we can also use the
black line to make predictions for example if someone told us they ate this much yam then we could use the black
line to predict how fast that person might run this is the predicted speed
the black line is a type of machine learning because we can use it to make predictions
in general machine learning is all about making predictions and classifications BAM
now that we can make predictions and classifications let's talk about some of the main ideas in machine learning
first of all in machine learning lingo the original data is called training data so the black line is fit to
training data alternatively we could have fit a green squiggle to the training data the green squiggle fits
the training data better than the black line but remember the goal of machine learning is to make predictions so we
need a way to decide if the green squiggle is better or worse than the black line at making predictions so we
find a new person and measure how fast they run and how much ham they eat and then we find another and another and
another altogether the blue dots represent testing data we use the testing data to compare the predictions
made by the black line to the predictions made by the green squiggle let's start by seeing how well the black
line predicts the speed of each person in the testing data here's the first person in the testing
data they ate this much yam and they ran this fast however the black line predicts that
someone who ate this much yam should run a little slower so let's measure the distance between
the actual speed and the predicted speed and save the distance on the right while we focus on the other people in the
testing data here's the second person in the testing data they ate this much yam and they ran
this fast but the black line predicts that they will run a little faster so we measure the distance between the
actual speed and the predicted speed and add it to the one we measured for the first person in the testing data
then we measure the distance between the real and the predicted speed for the third person in the testing data and add
it to our running total of distances between the real and predicted speeds for the black line
then we do the same thing for the fourth person in the testing data and add that distance to our running
total for the black line this is the sum of all the distances between the real and predicted speeds for the black line
now let's calculate the distances between the real and predicted speeds using the green squiggle remember the
green squiggle did a great job fitting the training data but when we are doing machine learning we are more interested
in how well the green squiggle can make predictions with new data so just like before we determine this
person's real speed and their predicted speed and measure the distance between them
and just like we did for the black line we'll keep track of the distances for the green squiggle over here
then we do the same thing for the second person in the testing data and the third person
and the fourth person this is the sum of the distances between the real and predicted speeds for the
green squiggle the sum of the distances is larger for the green squiggle than the black line in other words even
though the green squiggle fit the training data way better than the black line the black line did a better job
predicting speeds with the testing data so if we had to choose between using the black line or the green squiggle to make
predictions we would choose the black line BAM this example teaches two main ideas
about machine learning first we use testing data to evaluate machine learning methods second don't be fooled
by how well a machine learning method fits the training data note fitting the training data well but making poor
predictions is called the bias-variance tradeoff Ohno a shameless self-promotion if you want to learn more about the
bias-variance tradeoff there's a stat quest that will walk you through it one step at a time
before we move on you may be wondering why we used a simple black line in a silly green squiggle instead of a deep
learning or convolutional neural network or insert sir who is with bestest most fancy machine learning method here here
there are tons of fancy sounding machine learning methods and each year something new and exciting comes on the scene but
regardless of what you use the most important thing isn't how fancy it is but how it performs with testing data
double BAM now let's go back to the decision tree that we started with remember we wanted
to classify if someone loved stat quest based on a few questions to create the decision tree we collected data from
people who loved stat quest and from people who did not love stat quest altogether this was the training data
and we used it to build the decision tree got data from a few more people who love
stat quest and a few more people who did not love stat quest altogether this forms the testing data
we can use the testing data to see how well our decision tree predicts if someone will love stat quest the first
person in the testing data did not like silly songs so we go to the right side of the decision tree they didn't like
machine learning either so we just keep on going down the right side of the decision tree
they didn't like statistics either so the decision tree predicts that this person will not love stat quest however
this person loves stat quest so the decision tree made a mistake want wall the second person in the testing data
liked silly songs and that takes us down the left side of the decision tree they were also interested in machine learning
so we predict that that person loves stat quest and since this person actually loves stat quest the decision
tree did a good job hooray now we just run all of the other people in the testing data down the decision
tree and compare the predictions to reality then we can compare this decision tree to the latest greatest
machine learning method ultimately we pick the method that does the best job predicting if someone will love stat
quest or not triple bam in summary machine learning is all about making predictions and
classifications there are tons of fancy machine learning methods but the most important thing to know about them isn't
what makes them so fancy it's that we decide which method fits our needs the best by using testing data one last
thing before we go you may be wondering how we decide which data go into the training set and which data go into the
testing set earlier we just arbitrarily decided that these red dots were the training data
but the blue dots could have just as easily been the training data the good news is that there are ways to
determine which samples should be used for training data and which samples should be used for testing data
and if you're interested in learning more about this check out the stat quest and there are lots more stat quests that
walk you through machine learning concepts step-by-step so check them out hooray we've made it to the end of
another exciting stack quest if you like this stack quest and want to see more please subscribe and if you want to
support stack quest well consider buying one or two of my original songs or getting a t-shirt or a hoodie or some
other slick merchandise there's links on the screen and there's links in the description below alright until next
time quest on
The video defines machine learning as being all about making predictions and classifications. It uses examples like a decision tree to classify people (e.g., 'loves StatQuest') and a linear trend to predict running speed based on yam consumption, showing that models generalize patterns from data to make decisions on new inputs.
Training data is the original dataset used to build or 'fit' a machine learning model, while testing data is new, unseen data held back solely to evaluate the model's performance. The model’s true capability is judged by how it performs on testing data, not on the data it was trained on.
The video demonstrates the bias-variance tradeoff by comparing a simple 'black line' model that fits training data moderately but generalizes well, versus a complex 'green squiggle' that fits training data perfectly but makes poor predictions on new data. A model too tailored to training data (low bias, high variance) often fails to generalize, leading to larger errors on testing data.
The best model is selected by: 1) building it using training data, 2) testing it on held-out testing data, 3) calculating the prediction error by comparing predicted values to actual values, and 4) choosing the model with the smallest total error on the testing data. Performance on unseen data is the ultimate benchmark.
This example illustrates that a simple model (a 'black line' trend) might not perfectly align with training data points but creates reliable predictions on new data, whereas a complex model (a 'green squiggle') that overfits the training data generates larger prediction errors when tested. It reinforces that simplicity often beats complexity for generalization.
The video acknowledges that choosing which data goes into training vs. testing sets is critical but notes that arbitrary splits can lead to misleading performance evaluations. It recommends using formal methods like cross-validation (covered in other StatQuest videos) to ensure robust and reliable model assessment.
The decision tree classifies people into two categories ('loves StatQuest' or not) by asking sequential questions about preferences (e.g., liking silly songs, interest in machine learning or statistics). Each answer guides the tree to a leaf prediction, showing how a model uses features to make discrete predictions for new individuals.
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