What is Supervised Learning?
Machine learning is creating tremendous economic value today. I think 99 percent of the economic value created by machine learning today is through one type of machine learning, which is called supervised learning. Let’s take a look at what that means.
Supervised machine learning or more commonly, supervised learning, refers to algorithms that learn from x to y, or input to output mappings. The key characteristic of supervised learning is that you give your learning algorithm examples to learn from.
That includes the right answers, where by right answer I mean the correct label Y for a given input X. It is by seeing correct pairs of input X and desired output label Y that the learning algorithm eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output. Let’s look at some examples below.
If the input X is an email and the output Y is this email spam or not spam, this gives you your spam filter application. In the second line, if the input is an audio clip and the algorithm’s job is to output a text transcript, then this application is speech recognition. Also, if you want to input English and have it output to corresponding Spanish, Arabic, Hindi, Chinese, Japanese, or something else translation, then that’s machine translation.
The most lucrative form of supervised learning today is probably used in online advertising. Nearly all the large online advertising platforms have a learning algorithm that inputs some information about an advert and some information about you, and then tries to figure out if you will click on that ad or not.
Because by showing you ads they’re just slightly more likely to be clicked on. For these large online ad platforms, every click is revenue. This actually drives a lot of revenue for these companies. This is something I once done a lot of work on, maybe not the most inspiring application, but it certainly has a significant economic impact in some countries today.
If you want to build a self-driving car, the learning algorithm would take as input an image and some information from other
sensors such as a radar or other things, and then try to output the position of, say, other cars so that your self-driving car can safely drive around the other cars.
Take manufacturing as another example. You can have a learning algorithm takes as input a picture of a manufactured product, say a cell phone that just rolled off the production line, and have the learning algorithm output whether or not there is a scratch, dent, or other defect in the product. This is called visual inspection and it’s helping manufacturers reduce or prevent defects in their products.
In all of these applications, you will first train your model with examples of inputs X and the right answers, that is the labels Y. After the model has learned from these input, output, or X and Y pairs, they can then take cactusmeraviglietina.it a brand new input X, something it has never seen before, and try to produce the appropriate corresponding output Y.
Let’s dive more deeply into one specific example. Say you want to predict housing prices based on the size of the house. You’ve collected some data and say you plot the data and it looks like this.
On the horizontal axis is the size of the house in square feet. In the United States, people still use square feet. I know most of the world uses square meters. On the vertical axis is the price of the house in, say, thousands of dollars. With this data, let’s say a friend wants to know what’s the price for their 750 square foot house.