Supervised Machine Learning
Hello Learners !! I am Honey Darji. In this blog we understand about the what is supervised machine learning ? How we use this and some example of supervised learning.
Supervised Machine Learning is the most common topic in machine learning field. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
Supervised Machine Learning also classified into two groups. First is Regression and second is Classification.
- Regression : A regression problem is when the output variable is a real value, such as “Rupees” or “weight”.
2. Classification : A classification problem is when the output variable is a category, such as “White” or “blue” or “disease” and “no disease”.
Advantages:-
- Supervised learning allows collecting data and produce data output from the previous experiences.
- Helps to optimize performance criteria with the help of experience.
Disadvantages:-
- Classifying big data can be challenging.
- Training for supervised learning needs a lot of computation time.So,it requires a lot of time.
Let’s see example of the supervised machine Learning.
Suppose, we have different photos of animals. First we train our model Like If we have photo of horse so we teach our model , This is horse . In this we give the input and output of this photos. And then test our data with photos with inputs.
Thank you for reading blog.
