Types Of Supervised Learning Algorithms

SUPERVISED LEARNING = SUPERVISED + LEARNING

The word SUPERVISED can be seen as :

SUPERVISED = (In the supervision of) / (In the presence of a observer/supervisor)

The word LEARNING can be seen as :

LEARNING = (Machine acquire knowledge or learn using data)

So, we can define supervised learning as  ” Machine acquire knowledge in the presence of a supervisor (data whose answer is already known i.e. labeled data)”.
Supervised Learning is the sub branch of Machine Learning in which we train our Machine using labeled data.

https://en.wikipedia.org/wiki/Supervised_learning

Some supervised learning use cases such as:

1. Sentiment Analysis
2. Customer segmentation
3. Gene classification

Supervised learning further divided into 2 category

1. Classification Techniques
2. Regression

CLASSIFICATION TECHNIQUE:

It is a supervised learning technique in which the output is labeled i.e. output is as category such as ‘Male’ or ‘Female’, ‘PROFIT’ OR ‘LOSS’.

Classification is a problem that is used to predict which class a data point is part of which is usually a discrete value.

Example :

1. Determining whether or not someone will be a defaulter of the loan.
2. Decide email is span or not?
3. Decide whether comment is positive or negative.

Classification is again divided into three other categories

1. Binary classification
2. Multi-class/Multinomial classification
3. Multi-label classification.

REGRESSION:

It is a supervised learning technique in which the output is continuous value. it predicts a single output value using training data.

Example : Predicting the house price from continuous data, based on different features like size of a house, locality, floor, furnished or not, etc.

CLASSIFICATION ALGORITHMS:

1. Linear regression
2. Logistic regression
3. K-nearest neighbors
4. Artificial neural networks (ANN)
5. Neural Networks
6. Decision trees
7. Bayesian logic
8. Support vector machines (SVM)
9. Random forests

We will see each algorithm in details in subsequent articles …

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