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supervised vs unsupervised

Supervised learning

as the name indicates, has the presence of a supervisor as a teacher.

Basically supervised learning is when we teach or train the machine using data that is well labeled.

Which 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 the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.

Supervised learning: Machine is given the data

i.e trained before asking for required output.

For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all different fruits one by one like this:

Now suppose after training the data, you have given a new separate fruit, say Apple from the basket, and asked to identify it.

Types of Supervised Learning:

1) Classification: After training, if we show some new object with features, it says, in which class that new object belongs, out of predefined set of objects. e.g., cat or dog

2) Regression: After training, if we show new object with features, it gives us prediction about some value of label. e.g., house price prediction

Advantages:-

ü Supervised learning allows collecting data and produces data output from previous experiences.

ü Helps to optimize performance criteria with the help of experience.

ü Supervised machine learning helps to solve various types of real-world computation problems.

Disadvantages:-

ü Classifying big data can be challenging.

ü Training for supervised learning needs a lot of computation time. So, it requires a lot of time.

Unsupervised learning:

Without giving data, machine is asked for an appropriate output.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by itself.

For instance, suppose it is given an image having both dogs and cats which it has never seen.

Thus the machine has no idea about the features of dogs and cats so we can’t categorize it as ‘dogs and cats ‘. But it can categorize them according to their similarities, patterns, and differences, i.e., we can easily categorize the above picture into two parts. The first may contain all pics having dogs in it and the second part may contain all pics having cats in it. Here you didn’t learn anything before, which means no training data or examples.

It allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with unlabelled data.

Types of Unsupervised learning:

1) Clustering: Grouping a set of objects into a group( called cluster)

2) Association: Establishing relationship among various variables of our dataset

Semi-supervised learning: The best of both worlds

Can’t decide on whether to use supervised or unsupervised learning? Semi-supervised learning is a happy medium, where you use a training dataset with both labeled and unlabeled data. It’s particularly useful when it’s difficult to extract relevant features from data — and when you have a high volume of data.

Semi-supervised learning is ideal for medical images, where a small amount of training data can lead to a significant improvement in accuracy. For example, a radiologist can label a small subset of CT scans for tumors or diseases so the

Parameters

Supervised machine learning technique

Unsupervised machine learning technique

Process

In a supervised learning model, input and output variables will be given.

In unsupervised learning model, only input data will be given

Input Data

Algorithms are trained using labeled data.

Algorithms are used against data which is not labeled

Algorithms Used

Support vector machine, Neural network, Linear and logistics regression, random forest, and Classification trees.

Unsupervised algorithms can be divided into different categories: like Cluster algorithms, K-means, Hierarchical clustering, etc.

Computational Complexity

Supervised learning is a simpler method.

Unsupervised learning is computationally complex

Use of Data

Supervised learning model uses training data to learn a link between the input and the outputs.

Unsupervised learning does not use output data.

Accuracy of Results

Highly accurate and trustworthy method.

Less accurate and trustworthy method.

Real Time Learning

Learning method takes place offline.

Learning method takes place in real time.

Number of Classes

Number of classes is known.

Number of classes is not known.

Main Drawback

Classifying big data can be a real challenge in Supervised Learning.

You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known.

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