What is Supervised Learning in Machine Learning?
Simple and short explanation…With an in-depth continuation and links to the wiki…
TLDR:
Supervised learning is a type of machine learning algorithm that is the process of using labeled data to train algorithms to generate accurate predictions.
It is a type of artificial intelligence (AI) that allows machines to learn from data sets that are labeled with known outcomes.
Supervised learning is typically used for predicting future outcomes, such as whether a given piece of data belongs to one category or another.
Examples include spam filters, speech recognition, machine translation, online advertising, self-driving cars, and visual inspection.
What is machine learning?
Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed.
Arthur Samuel decided to write a program that maybe can teach itself to play checkers in the 1950s.
The program played tens of thousands of games against itself and was able to learn which positions resulted in wins and which resulted in losses. Over time, the program became a better checker player than Samuel himself. The more opportunities a machine learning algorithm has to learn, the better it will perform.
Next, we will talk about types of supervised learning.
Regression: A type of supervised learning
Regression is a supervised learning technique used to predict a numerical value from a given set of data.
It is one of the most important techniques in machine learning and predictive analytics.
Regression is used in a wide variety of applications, including finance, economics, and marketing.
It can be used to identify relationships between factors, such as the impact of advertising on sales.
Regression is often used to automatically detect patterns in data that can be used for predictive purposes.
It is used to estimate a best-fit line or curve (called a regression line or curve).
The regression line or curve is then used to estimate the value of an unknown point on the same line or curve.
Classification: What if you know the categories?
So regression can be used to predict a value yet unknown in the data set.
But if we have a limited number of categories? Then it is called classification or categorization (the same).
Classification is a type of supervised learning that is used to assign a data point to one of the predefined sets of classes or labels.
For example, a classification model could be used to classify images into different categories such as animals, plants, or buildings.
It can also be used to classify text documents into different categories such as sentiment (positive or negative) or topic (sports, politics, etc).
Feel free to skip examples if you get the concept…
Example of classification usage in Breast Cancer Recognition
Say you’re building a machine learning system so that doctors can have a diagnostic tool to detect breast cancer.
This is important because early detection could potentially save a patient’s life.
Using a patient’s medical records your machine learning system tries to figure out if a tumor that is a lump is malignant meaning cancerous or dangerous.
Or if that tumor, that lump is benign, meaning that it’s just a lump that isn’t cancerous and isn’t that dangerous?
So maybe your dataset has tumors of various sizes.
And these tumors are labeled as benign, which we can designate in this example with a 0.
Or malignant, which will designate in this example with a 1.
You can then plot your data on a graph where the horizontal axis represents the size of the tumor and the vertical axis takes on only two values 0 or 1 depending on whether the tumor is benign 0, or malignant 1.
One reason that this is different from regression is that we’re trying to predict only a small number of possible outputs or categories.
In this case, two possible outputs are 0 or 1, benign or malignant.
This is different from regression which tries to predict any number, all of the infinitely many numbers
This example is taken from an awesome Andrew NG Coursera Course on Machine Learning.
What’s next?
If there is Supervised Learning then there should be Unsupervised Learning. They are both Super-Useful:)
Can you guess how it differs?
Read my article in Unsupervised Learning in ML to get a better understanding.
***
Here is my short overview article of basic things about chatGPT.
If you want to understand more about how chatGPTworks — check out this article.
And if you want to get inspiring prompts — here is a compilation of usage of chatGPT.
***
“Alone we can do so little; together we can do so much.” — Helen Keller
Please, help me to achieve my dreams — check out
my Patreon page.
Or sent just a small tip to support my humble living.
Any amount is highly appreciated and used only for making free public goodies.
Thank you 🙏
