Broadly speaking one can simply categorise ML algorithms into following groups: 1. Supervised Learning : Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X) = a1x1 + a2x2+a3x3+.....+ anxn
where our goal is to find the values of a1,a2,a3,....,an such that for every value of input(x1,x2,x3,....xn) we can predict the output Y( continuous or categorical). Further in supervised learning one can use ML algorithms as per their problem statement and output required. For example : Determine the price of stock (continuous variable) from set of independent variable then in this case one can use Regression which is type of supervised algorithm.
- Unsupervised Learning : Unsupervised learning is where you only have input data (X) and no corresponding output variables.The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. There is no labelled output to map the function that describe relation between input and output.
For example: Market segmentation is one such problem statement where one can use unsupervised algorithms like clustering to get different segmentation based on homogeneity.
Other examples of unsupervised algorithms are PCA, Association rules, anomaly detection,etc.
Note: In some real life scenario, chances are there where problem is mixed of both i.e. few of the data have label and rest do not have and in such cases one needs to deploy semi-supervised techniques to find the solution.
manpreet![Tuteehub forum best answer]()
Best Answer
2 years ago
While studying about machine learning, I've learnt the importance of defining your problem before getting started trying to model it.
I can see 2 types of problem categorification:
Example definitions found on the net:
First type:
Second type:
Do each type of categories have a name ? And are these types correlated or independent ?