Machine Learning Paradigms – Explain Types of ML Classification

Machine Learning (ML) is an application where algorithms can learn from experience without being explicitly programmed. Our algorithm receives input data that we want it to analyze, and it then outputs what it has discovered from our input. What distinguishes ML is the element of learning. It resembles a mysterious machine that accepts input, performs magic inside, and then outputs the results we are looking for. Still, like humans, machines can take different approaches to learning the material. These different approaches are called ML or machine learning paradigms and help us understand how a computer learns from data, specifically from the input.

Types of Machine Learning Paradigms

Machine learning is generally classified into three basic ML paradigms:-

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Supervised Learning

Similar to concept mapping, supervised learning is one of the types of machine learning paradigms. You put something in, and you receive something back. A function that tries to abstract the system to have rules that probably don’t make sense to a natural human is produced when you input data and evaluate it. The answer you were hoping for was “yes” because the image shows a car. You receive a no since there are no cars in this picture. With more precision as it learns, the system can rule out objects other than automobiles.

You can’t truly give a straight explanation of a certain idea when teaching it and expect your students to understand it. You explain how the concept works while also providing examples. They infer their connections between them based on the assumption that if you have this input, you will get this certain outcome. Don’t describe a dog’s legs, eyes, ears, or other features to a young child when explaining what a dog is; instead, point and say, “That’s a dog,” or, “That’s not a dog,” until they understand. You give a task to do along with samples of both the right and wrong responses.

For our automobile example, this would imply that the system abstracts the pattern after we have codified the photos, whether or not they contain a car. The biggest drawback is that it only uses training data. If all your images are red cars in a forest for a set of cars, a tomato with a green wheel might be determined to be a car because it has a red shape and green around it, or the blue car might register as a false negative. He found a correlation that fit the pattern without matching the pattern we wanted.

Benefits of Supervised Machine Learning

• A high degree of control. The model trainer provides the data and rules for what the model will learn.
• Easy and intuitive to understand. Correct predictions of training data are easy for most people to understand.
• Suitable for understanding relationships between input and output data if both can be provided.

Disadvantages of Supervised Machine Learning

• By definition, supervised learning models must provide both input and output data.
• Supervised machine learning algorithms require a data set with inputs and outputs to train. This data can be challenging to obtain or create.
• The developer may want the model to outperform the human player, in which supervised machine learning is not the correct paradigm (see reinforcement learning).
• There is always a risk of overfitting models and forecasts.
• Supervised learning requires manual algorithm selection. Given the problem, it is necessary to choose the correct algorithm for manual use.

Our Learners Also Read: Top Ranking Machine Learning Algorithms 

Unsupervised Learning

Unsupervised learning is a type of machine learning paradigm in that instead of evaluating the accuracy of the training, you assess the results of the process. You specify the set and supervise the process of obtaining results using supervised learning. Still, you feed the set and let it be classified by the system to determine whether the new input is a member of the derived group or not.

This type of self-organized ML paradigm can lead to exciting results. You let the system work things out so that the clusters make sense based on the requirements. For example, if someone handed you a bunch of vegetables and asked you to sort them, and you couldn’t tell what it was or the purpose of the sorting, you could lose color. Potatoes, carrots, etc., are different colors, but they are the same, although your sorting method may not agree. You provide the data and let the system make sense of it based on some vague initial assumption implied in the data.

This machine learning paradigm is helpful in something like a medical application. If you feed a lot of similar cell preparations to the algorithm, it can find something that correlates with cancer, which can be investigated further. While in supervised learning, we gave the system the desired categories for output, here we provided the system our input and let it make sense of it. You might find something you didn’t know what to look for as an output with this kind of system.

Advantages of Unsupervised Learning

• It does not require data marking.
• A suitable methodology for clustering and finding patterns and structures in raw untagged data.
• The performance of some supervised applications can be enhanced by using it as a preprocessing step before classifying data for a supervised learning run.
• It can be used for dimensionality reduction, which is advantageous when the number of supervised learning features is high.

Disadvantages of Unsupervised Learning

• Computations based on trial and error are often more time-consuming than supervised learning.
• Compared to a model trained on labeled data, the results may be less accurate, e.g., identified patterns.

Reinforcement Learning

The third type of machine learning paradigm or machine learning classification is Reinforcement learning. It is a type of ML algorithm where a computer learns by trying things out and getting feedback on its actions. As your algorithm explores its environment, it discovers more and strives to achieve specific goals for which it is rewarded. The closer he gets to the destination, the more he is awarded. Your algorithm responds to the environment it is in to grow and adapt to the system’s rules.

A real example is teaching a dog to walk on a leash. You don’t want the dog to drag or fall behind, but the dog doesn’t understand the rules. When the dog does what it is supposed to, you reward it and leave. When they don’t, they can’t continue walking, which shows them they’ve broken the rules (ideally, you don’t punish the dog for no reason because they don’t understand, but it’s okay to do the algorithm). A dog learns the rules by trial and error and eventually instinctively knows what it can and can’t do when you have its leash.

Your algorithm adapts to the changes that come in rather than based on a set of static inputs. Anything that doesn’t entail a continuous process (such as a robot trying to walk while resisting gravity) or a dynamic environment (such as a video game algorithm where stable states don’t exist) makes less sense in terms of this. Instead of using a predetermined data set, this learning method relies on trial and error.

Benefits of Reinforcement Machine Learning

• Does not require training data to work.
• It can be used in uncertain environments with little information.
• Models improve with experience.
• Models can perform better than the human who wrote them, as already demonstrated by A.L. Samuel in 1959 with a chess-playing algorithm.

Disadvantages of Reinforcement Machine Learning

• More straightforward problems could be better solved by a supervised or unsupervised machine learning approach.
• It may require a lot of processing power.
• At its core, it assumes that the environment is a Markov model with probabilistic states and state transitions; this may not always be practical.
• The cost of learning can be high, especially if the learning agent is, e.g., a robot that needs to learn for a long time and may also require, e.g. hardware maintenance.

Conclusion

This blog discusses various paradigms of machine learning in an understandable language and from a technical point of view, which makes it easier to understand the subject, put it into practice and confidently answer any question related to the topic in interviews.

Three different machine learning paradigms help solve different categories of problems:-

• With supervised learning, fresh input instances are labeled correctly after learning from a dataset of previously labeled examples. It resembles learning in a classroom.
• Finding patterns and underlying relationships from unlabeled data is the goal of unsupervised learning. It resembles self-directed learning.
• When an autonomous agent is placed in an unfamiliar environment, reinforcement learning occurs as it discovers the best course of action through trial and error as well as reward and punishment systems.

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