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What is Data Classification in Machine Learning?

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data classification

Data classification is a fundamental concept in machine learning without which most ML models simply couldn’t function. Many real-world applications of AI have data classification at the core – from credit score analysis to medical diagnosis. So how does it work? 

That’s what we’ll discuss in this article. We will explain what classification in ML is, how it differs from clustering, and the applications and different types of classification algorithms. But before we get to that, we should discuss another fundamental concept – supervised learning.

Explaining Supervised Learning

supervised learning

To properly discuss data classification in ML, we should first give a short overview of supervised learning. In short, supervised learning is an approach in machine learning where a model learns from example data paired with correct labels.

The goal is to train the model to identify patterns and relationships in the input variables, allowing it to make accurate predictions or classifications. In supervised learning, the algorithm is provided with a dataset consisting of input variables and corresponding correct labels.

Then, the algorithm analyses the relationship between the input variables and the assigned labels to understand how they are related. It then uses this understanding to make predictions on new, unseen data.

But let’s step away from the theoretical sphere into the practical. Some examples of supervised learning are spam detection, speech recognition, and object recognition.

In spam detection, the algorithm is trained on a dataset of labeled emails (spam or not spam) and learns the distinguishing features of each class. This enables it to accurately classify incoming emails as spam or not spam.

Similarly, in speech recognition, the algorithm learns from a labeled dataset of audio samples and their corresponding transcriptions. By analyzing the patterns in the audio, the model can accurately transcribe spoken words or phrases.

In object recognition, the algorithm is trained on a dataset of images with labeled objects. By analyzing the visual patterns in the images, the model can accurately identify and classify objects in new, unseen images.

So, how does that relate to data classification?

What is Classification?

Classification is a fundamental task in machine learning that involves categorizing data into different classes or categories based on their features or characteristics. It is a supervised learning approach (hence the answer to the question posed above) where algorithms are trained on labeled datasets, with input variables and corresponding class labels.

The goal of classification is to build a model that can accurately predict the class or category of unseen data based on the patterns and relationships learned from the training data. Just like supervised learning in general, classification is widely used for spam detection, speech recognition, object recognition, sentiment analysis, medical diagnosis, etc.

It plays a crucial role in enabling machines to make informed decisions and automate processes based on the input data’s classification. If we were to boil down data classification in machine learning to its very basics, then we could say that classification is a type of pattern recognition.

Classification vs. Clustering in Machine Learning

Here, we also need to make a distinction between classification and clustering in machine learning, as they serve different purposes and operate on different types of data. As we stated multiple times, classification is a technique that deals with labeled data and is always related to supervised learning.

It aims to assign data points to predefined classes or categories based on their features. Classification algorithms learn from a labeled training dataset and build a model that can predict the class of unseen instances.

Conversely, clustering is an unsupervised learning technique that deals with unlabelled data. It aims to group similar data points together based on their inherent similarities or patterns. Clustering algorithms do not rely on predefined classes or labels but analyze the data to identify natural clusters or groups.

The most commonly used clustering algorithms are k-means clustering, hierarchical clustering, and density-based clustering. Clustering can be used in various domains, such as customer segmentation, image recognition, and anomaly detection.

The choice between classification and clustering depends on the nature of the data, the problem at hand, and the desired outcome.

The Multiple Applications of Classification Algorithms

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Classification algorithms serve the purpose of categorizing new observations or instances into different predefined classes or groups. By analyzing the features or attributes of these instances, a classification model is built through supervised learning. This model is then used to predict the class of unseen data points.

The applications for classification algorithms are diverse and span across various fields. Besides the ones we already touched on, like speech recognition and spam email detection, classification algorithms also find use in the pharma industry, for medical diagnosis, and biometric authentication, to just name a few.

Regarding pharma, classification algorithms assist in classifying different drugs based on their chemical properties, side effects, and therapeutic uses. In the field of medical diagnosis, these algorithms can contribute to cancer tumor cell identification, aiding in the classification of tumor cells as benign or malignant.

Classification algorithms are also employed in biometric authentication systems, where they classify individuals based on unique biological characteristics like fingerprints, iris patterns, and voiceprints, ensuring secure identification and access control.

In short, classification algorithms play a crucial role in categorizing new observations into different, predefined classes or groups. But we should also discuss two different types of learners of algorithms for classification.

Lazy vs. Eager Learners

Lazy learners and eager learners are two types of machine learning algorithms that differ in their approach to training and prediction processes. Lazy learners, also known as instance-based learners or memory-based learners, do not learn a specific model during training.

Instead, they store the training instances in memory and use these stored instances for making predictions. When a new instance needs to be classified, a lazy learner compares it to the stored instances and predicts the label based on the most similar instances. Some examples of lazy learners are k-nearest neighbors (KNN) and case-based reasoning.

In contrast, eager learners, also referred to as model-based learners, build a specific model during the training process using the training data. This model represents the learned knowledge and can be directly used for making predictions on new instances.

Eager learners typically require more computational resources during the training phase compared to lazy learners. Some examples of eager learners are decision trees and Naive Bayes algorithms – and here, we can provide an even more concrete example. ChatGPT primarily uses a Naïve Bayes algorithm for text classification, so you can clearly see how widespread it is.

The main advantage of lazy learners is their ability to adapt quickly to changes in the training data, as they do not require a retraining process. They can handle complex problems with large training sets more efficiently. However, lazy learners can be computationally expensive during the prediction phase.

Eager learners, on the other hand, are generally faster during the prediction phase as they use the trained model directly. They are suitable for problems with a large number of features and are particularly effective in handling structured and tabular data.

From Data Classification to Bespoke AI Solutions

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If you are reading about data classification in machine learning because you are considering utilizing AI for a project you have, Aya Data can help. We provide services across the entire AI value chain – from data acquisition through annotation to building and deploying bespoke AI models.

In case you need someone to create a data classification algorithm or need to label data for one that is already in the works, we can do that for you. Feel free to schedule a consultation with one of our experts so that we can discuss the topic in more detail.