Types of Machine Learning Algorithms

Introduction to the Types of Machine Learning Algorithms.

Machine learning algorithms are a set of instructions for a computer on how to interact with, manipulate, and transform data. There are so many types of machine learning algorithms. Selecting the right algorithm is both science and art.

Two data scientists tasked with solving the same business challenge can choose different algorithms to address the same problem. However, understanding different classes of machine learning algorithms helps data scientists identify the best types of algorithms. In this article, I will introduce you to the main types of machine learning algorithms.

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Types of Machine Learning Algorithms

Bayesian:

Bayesian algorithms allow data scientists to encode past beliefs about what models should look like, regardless of the state of the data. With so much focus on the data defining the model, you might wonder why people would be interested in Bayesian algorithms.

These algorithms are especially useful when you don’t have huge amounts of data to train a model with confidence. A Bayesian algorithm would make sense, for example, if you have prior knowledge of part of the model and therefore can code it directly.

Take the case of a medical imaging diagnostic system that looks for lung disorders. If a study published in a journal estimates the likelihood of different lung disorders based on lifestyle, those probabilities can be encoded into the model.

Clustering:

Clustering is a fairly easy technique to understand – objects with similar parameters are grouped (in a cluster). All objects in a cluster are more similar to each other than objects in other clusters.

Clustering is a type of unsupervised learning because the data is not labelled. The algorithm interprets the parameters that make up each element, then groups them accordingly.

Decision tree:

Decision tree algorithms use a branching structure to illustrate the results of a decision. Decision trees can be used to map the possible outcomes of a decision. Each node in a decision tree represents a possible outcome. Percentages are assigned to nodes based on the likelihood of the outcome occurring.

Decision trees are sometimes used for marketing campaigns. You might want to predict the outcome of sending a 20% coupon to customers and prospects. You can divide customers into four segments:

  • Persuading who will likely buy if they receive awareness
  • Safe things that will buy no matter what
  • Lost causes that will never buy
  • Fragile clients likely to react negatively to an outreach attempt

If you are sending out a marketing campaign, you want to avoid sending articles to three of the groups because they won’t respond, but anyway, or respond negatively.

Targeting the persuaders will give you the best return on investment (ROI). A decision tree will help you map these four customer groups and organize prospects and customers according to who will respond best to the marketing campaign.

Dimensionality Reduction:

Dimensionality reduction helps systems remove data that is not useful for analysis. This group of algorithms is used to remove redundant data, outliers, and other unnecessary data.

The dimensionality reduction can be useful when analyzing sensor data and other Internet of Things (IoT) use cases. In IoT systems, there can be thousands of data points just telling you that a sensor is activated.

Storing and analyzing this “on” data is not useful and will take up significant storage space. Moreover, by removing these redundant data, the performance of a machine learning system will improve. Finally, reducing dimensionality will also help analysts visualize the data.

Instance-Based Machine Learning:

Instance-based algorithms are used when you want to rank new data points based on similarities to training data. This set of algorithms is sometimes referred to as lazy learners because there is no training phase.

Instead, the instance-based algorithms simply match the new data with the training data and rank the new data points based on their similarity to the training data.

Instance-based learning is not well suited to datasets with random variations, irrelevant data, or data with missing values. Instance-based algorithms can be very useful in pattern recognition.

For example, instance learning is used in chemical and biological structure analysis and spatial analysis. Analysis in the biological, pharmaceutical, chemical and technical fields often uses various instance-based algorithms.

Neural Networks:

A neural network attempts to mimic the way a human brain approaches problems and uses layers of interconnected units to learn and infer relationships based on observed data.

A neural network can have multiple layers connected. When there is more than one hidden layer in a neural network, it is sometimes referred to as deep learning. Models of neural networks can adapt and learn as data changes.

Neural networks are often used when data is unlabeled or unstructured. One of the main use cases for neural networks in computer vision.

Deep learning is exploited in a variety of applications today. Self-driving cars use deep learning to help the vehicle understand the environment around the car.

When cameras capture images of the surrounding environment, deep learning algorithms interpret the unstructured data to help the system make near-real-time decisions. Likewise, deep learning is built into the applications radiologists use to help interpret medical images.

Regression:

Regression algorithms are commonly used for statistical analysis and are key algorithms for use in machine learning. Regression algorithms help analysts model relationships between data points.

Regression algorithms can quantify the strength of the correlation between variables in a data set. Additionally, regression analysis can be useful in predicting future data values ​​based on historical values.

However, it is important to remember that regression analysis assumes that correlation is related to causation. Without understanding the context around the data, regression analysis can lead you to inaccurate predictions.

Regularization:

Regularization is a technique for modifying models to avoid the problem of overfitting. You can apply regularization to any machine learning model. For example, you can regularize a decision tree model.

Regularization simplifies models that are too complex and likely to be over-adjusted. If a model is overfitted, it will give inaccurate predictions when exposed to new data sets.

Overfitting occurs when a model is created for a specific dataset but will have poor predictive capabilities for a generalized dataset.

Rule-Based Machine Learning:

Rule-based machine learning algorithms use relational rules to describe data. A rules-based system can be compared to learning systems automatic weaving that creates a pattern that can be generally applied to all incoming data.

In the abstract, rule-based systems are very easy to understand: if X data is entered, do Y. However, as systems become operational, a rules-based approach to machine learning can become very complex.

For example, a system might have 100 predefined rules. As the system encounters more and more data and is trained, hundreds of rule exemptions could likely emerge.

When creating a rules-based approach, it is important to be careful that it does not become so complicated that it loses its transparency. Think about the complexity of creating a rules-based algorithm to enforce the tax code.

These were therefore the main types of machine learning algorithms. Hope you liked this article on the types of algorithms in Machine Learning. Please feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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