Supervised Learning is a type of algorithm that contains well-categorized data, and based on the statistics of that data, machines predict very accurate results. The information fed into the machine has a labeled nature, which means it has some connection with the output.
The name supervised Learning is given to this algorithm because the data fed into the machine acts as a mentor or guide towards them, helping them in their calculations to churn out the correct result.
TYPES OF SUPERVISED LEARNING
There are two types of supervised Learning:
REGRESSION
CLASSIFICATION
REGRESSION
This model is used when the output we are expecting is based on uninterrupted variables, and there is a direct connection between the input and output variable. It can predict stock market trends, weather patterns, property prices, etc.
Its subcategories include:
Linear Regression
Regression Trees
Polynomial Regression
Nonlinear Regression
CLASSIFICATION
This model is used when the output variable is categorized into two categories as male-female, red-blue, disease-no disease, yes-no, etc.
Its subcategories include:
Random Forest
Decision Trees
Logistic Regression
Support Vector Machines
PROS OF MACHINE LEARNING
It helps in generating output based on past experiences. It collects data from the expertise and optimizes the work accordingly, thereby producing accurate results
Real-world problems can be solved, such as fraud detection
CONS OF MACHINE LEARNING
Complex data problems are difficult to solve through this model
It is time-intensive process