Supervised learning is a type of machine learning where a model is trained
to make predictions based on labeled data.
The model is given input data and the corresponding correct output and it learns to predict the output for new data based on this training
This allows the model to perform the tasks such as classification and regression.
Supervised learning is a way that we can teach computers
to do things by showing them examples and telling them the right answers.
For example, let’s say we want to teach a computer to recognize pictures of dogs.
We can show it pictures of different breads of dogs and tell it the name of the bread.
Then the computer will try to figure out the characteristics
Typically go with each breed of dog.
Once the computer has learned enough different breads of dogs,
we can test it by showing it a picture of a dog it has never seen before.
The computer will use what it has learned to try to guess which bread of dog it is.
If it guesses correctly, we can say that the computer did a good job of learning about dogs.
It does not guess correctly, we can give it more examples to help learn even better.
Supervised learning uses a training set to teach models to yield the desired output.
This training dataset includes inputs and correct outputs, which allow the model to learn over time.
The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.
Supervised learning can be separated into two types of problems when data mining
-classification and
regression
Classification uses an algorithm to accurately assign test data into specific categories.
It recognizes specific entities within the dataset and attempts to draw some conclusions on how those entities should be labeled or defined.
Regression is used to understand the relationship between dependent and independent variables.
It is commonly used to make projections, such as for sales revenue for a given business.
Linear regression, logical regression and polynomial regression are popular algorithms.
Common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest.
Негізгі бет supervised learning in Machine Learning
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