Supervised machine learning is probably a term you have never heard before. However, it isn’t as complex as it sounds. In supervised machine learning, you train a machine using “labelled data”. Labelled data is basically data that already has an answer. In other words, an input value that you already know the output value for would be considered labelled data. You might be thinking, why exactly is this useful? Well, a supervised learning algorithm learns from labeled data and helps you make predictions for future data. Using the known data to find a pattern for future data is part of the job of a data scientist, and creating a model takes a lot of time and expertise. Machines can also learn over time utilizing this model. Although supervised machine learning seems so easy to use, there are a few drawbacks that make other machine learning methods more preferable. First of all, supervised machine learning models are difficult to apply to big data, since datasets can have a high possibility of human error, resulting in algorithms learning incorrectly. Furthermore, as said before, developing the model can take a lot of time and advanced knowledge to determine.
Supervised learning can also be split into two categories of problems, classification and regression. Classification problems use an algorithm to be sorted into categories. An example of this in the real world is classifying spam as a separate folder in your inbox. Regression problems on the other hand use an algorithm to understand the relationship between independent and dependent variables. Regression techniques can predict a future output using preexisting data, such as determining the price of a car given that the value decreases each year. If you think back to Algebra, you may remember linear regression, and finding the equation of the relationship between x and y. Supervised machine learning uses the same concept, however the model may not always be linear. Overall, supervised machine learning is actually used in a lot of real life situations, and chances are you have used supervised machine learning before without even realizing it. Your brain uses supervised learning all the time, when estimating how long it will take to get somewhere. You instinctively know that if it raining outside, it will take longer to drive home. This is because you have learned from past experiences that rain makes it harder to drive. If you continuously keep track of how much longer driving home in the rain takes compared to regular conditions, you can create a supervised learning model. This can help you predict your commute time for future encounters.
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