What Is Supervised Learning?
Supervised learning is an algorithm used in machine learning and it is the most common algorithm. it thus takes data sample data for training the machine.
the data however describes as training data and target value which is the outcome we need. the target data can be label data or any kind of response with the data sample.
The purpose of this algorithms is to map input data and target data during the training phase over numerous data.
so, Lets say, we have
x: Input variables
Y: Output variable or target
so, let’s use a mapping function to map input and target variables.
Y= f(x)
Now, the above function is use for mapping where when we get new data which is (x) that we can map to corresponding output variable (Y).
So thus it refers as we are doing this function just like a supervisor where we are training new data also not to forget that we have output in the form of labelled or response. supervised machine learning algorithms are Decision tree, Random Forest, KNN, Logistic Regression etc.
Based on the given requirement the Supervised learning can be classified as follows:
- Regression
- Classification
1. Regression
Regression algorithms are used if there is a relationship between the input variable and the output variable. thus, use for the prediction of continuous variables.
however, Below are some popular Regression algorithms which come under it:
- Linear Regression
- Regression Trees
- Non-Linear Regression
- Bayesian Linear Regression
- Polynomial Regression
2. Classification
Classification algorithms are used when the output variable is categorical, which means there are two class such as Yes/No,true/False etc,
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Advantages of Supervised learning:
- With the help, on the basis of prior experiences the model can predict the output.
- In this , we are sure about what output can be there.
- this however help us to solve various real-world problems such as fraud detection, spam filtering, etc.
Disadvantages of supervised learning:
- Supervised learning models are not suitable for handling the complex tasks.
- It cannot predict the correct output if the test data is different from the training dataset i.e. test and training data should be of the same type.
- Lots of computation times required during Training.
- In this, we need enough knowledge about the classes of objects.
Supervised Learning vs unsupervised learning
| Supervised Learning | Unsupervised Learning |
| Supervised learning algorithms are trained using labeled data. | Unsupervised learning algorithms are trained using unlabeled data. |
| this model takes direct feedback to check if it is predicting correct output or not. | Unsupervised learning model does not take any feedback. |
| this model predicts the output. | Unsupervised learning model finds the hidden patterns in data. |
| In supervised learning, input data is provided to the model along with the output. | In unsupervised learning, only input data is provided to the model. |
| The goal of supervised learning is to train the model so that it can predict the output when it is given new data. | The goal of unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset. |
| Supervised learning needs supervision to train the model. | Unsupervised learning does not need any supervision to train the model. |
| it can be categorize in Classification and Regression problems. | Unsupervised Learning can be classified in Clustering and Associations problems. |
| thus, it can be used for those cases where we know the input as well as corresponding outputs. | Unsupervised learning can be used for those cases where we have only input data and no corresponding output data. |
| Supervised learning model produces an accurate result. | Unsupervised learning model may give less accurate result as compared to supervised learning. |
| it is not close to true Artificial intelligence as in this, we first train the model for each data, and then only it can predict the correct output. | Unsupervised learning is more close to the true Artificial Intelligence as it learns similarly as a child learns daily routine things by his experiences. |
| It also includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. | It thus includes various algorithms such as Clustering, KNN, and Apriori algorithm. |
[1]https://www.tutorialspoint.com/machine_learning/machine_learning_supervised.htm
written by: Nikesh Maurya
Reviewed By: Krishna Heroor
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