1. Supervised learning
Hi! My name is Hadrien, I'm a Content Developer here at DataCamp. Great job on Chapter 1! Now that you know more about machine learning, let's dive in deeper.
2. Modeling
Machine Learning is all about modeling. Not that kind of modeling.
3. Types
As you saw in Chapter 1, there are several types to machine learning, including supervised learning.
4. What is supervised learning?
Supervised learning is basically a labeling machine. It takes an observation, and assigns a label to it.
5. Classification and regression
There are two flavors of supervised learning: classification and regression.
6. Classification
Let's focus on classification first.
7. Classification
Classification consists in assigning a category to an observation. We're predicting a discrete variable, a variable that can only take a few different values. Is this customer going to stop its subscription or not? Is this mole cancerous or not? Is this wine red, white or rosé? Is this flower a rose, a tulip, a carnation, a lily?
8. Observations
Remember, we feed the model observations. Let's take college admissions where we want to predict acceptance.
9. Features
For simplification, we show two features here: GPA and admission test results. We could have more features like: involvement in student organizations and sports or prizes applicants have won.
10. Target
And, the target is what we want to predict. There are two possible labels: those who are accepted and those who are not. The target can only be one of these two labels, making it a classification problem.
11. Graphing our data
Here are our observations plotted. GPA on the x axis and test results on the y axis. Blue points represent accepted applicants, red points represent rejected applicants.
12. Splitting data
We keep 80% of our data to train our model.
13. Manual classifier
Because we use just two features, we're able to plot and interpret the results. For us humans, it's pretty clear. If you score above 4 on both the GPA and entrance test, you're accepted.
If we added more features like extracurriculars or prizes, we would need more axes. It would be very hard for us to interpret the data with our human eye.
However, an model wouldn't struggle at all. We can use a Support Vector Machine. It sounds scary, but it's just a line separating our points.
14. Support vector machine - linear classifier
We train our algorithm and classify the 20% of observations we left aside.
It only misclassifies two blue points as red - meaning two applicants were wrongly predicted as rejected. The problem is, it tries to separate with a straight line, so it's unlikely to do better than that.
15. Support vector machine - polynomial classifier
What if we allow a curved line instead?
Then it classifies everything correctly! There are ways we can tweak our model's behavior, like allowing curves. We will talk about that later.
16. Regression
Now, what about regression?
17. Regression
While classification assigns a category, regression assigns a continuous variable, that is, a variable that can take any value. For example, how much will this stock be worth? What is this exoplanet's mass? How tall will this child be as an adult?
18. Predicting temperature
Let's look at some new data: weather readings. Can we predict temperature based on humidity?
19. Training data
We use 80% of the data to train our model. Seems like when humidity rises, temperature decreases.
20. Linear regression
Indeed, our linear regression model catches that.
21. Model
Making this is our model.
22. Given humidity...
According to our model, if the humidity rate is 0.5,
23. ...find temperature
Then the temperature is 18.5 ?C.
24. Testing data
And this is how the model performs on real data. It identified the trend, but is still bad at predicting. More features, like wind, cloudiness, location, season could make it more accurate.
25. Classification vs regression
It's up to you to choose whether you want to frame your problem as a regression or classification problem. For example, we could predict an exact temperature,
or a categorical range of temperatures like "Cold", "Mild", and "Hot". This is true for something like age, where we can have categories like baby, child, teenager, adult, and so on! Do you see the difference?
26. Let's practice!
Let's make sure everything is clear with some exercises.