Example
- See also: Machine learning terms
Introduction
In machine learning, examples or training data are input data and the corresponding desired output. A single example is the values of one row of features and possibly a label. Examples in supervised learning fall into two general categories: labeled examples and unlabeled examples. Labeled examples comprise one or more features and a label. Labeled examples are used during training. On the other hand, unlabeled examples consist of features but no label. Unlabeled examples can be utilized during training and inference.
What is an example in machine learning?
An example in machine learning refers to a pair of input and output values used to train a model. The input value is made up of features or attributes that describe an object or phenomenon, while the output value serves as its label or class. For instance, spam detection systems typically take email messages (input) as their label (output), which could either be "spam" or "not spam." Similarly, image recognition systems take pictures as inputs and assign them labels describing what the image depicts. Suppose we collected a dataset of 2000 apartments with their features and prices. The features and the price of a single apartment would be an example.
| Features | Label | ||
|---|---|---|---|
| Location | Bedroom # | Bathroom # | Price |
| Midtown | 3 | 2 | $800,000 |
| Uptown | 2 | 2 | $500,000 |
| Downtown | 2 | 1 | $700,000 |
| Features | ||
|---|---|---|
| Temperature | Precipitation | Humidity |
| 25 | 9 | 12 |
| 20 | 54 | 32 |
| 31 | 0 | 87 |
Explain Like I'm 5 (ELI5)
An example in machine learning is a set of instructions we give the computer to learn something. For instance, if we want it to recognize different types of fruit, we could show it pictures of apples, oranges and bananas - each picture serving as an example for what the computer should search for when trying to recognize fruits.
By providing the computer with multiple examples, it can begin to recognize patterns in data and even learn to recognize fruit on its own. It's like how you can recognize a dog even if you haven't seen that particular breed before because you know its look from all of your previous pictures and experiences with dogs.