Table of Contents
- What Is the Difference Between Supervised and Unsupervised Learning?
Today, companies use machine learning on a massive scale to enhance their business operations. A good example is Netflix, where ML helped save $1 billion due to customized suggestions on the platform, and it also has had a tangible impact throughout the pandemic.
Such companies make use of various ML solutions that are applied within the main fields of AI, like computer vision (CV) and natural language processing (NLP). Here, ML helps them cut operational costs, boost customer experience, and drive value from new revenue streams. Thus, businesses that invest in machine learning stand to reap significant revenue and cost benefits. Yet, those who are late to adopt ML efforts, lose the opportunity to profit from its intrinsic capacity to learn and offer growing value over time.
As AI continues to transform the business strategies of enterprises by providing them with large volumes of data, it’s up to the companies to learn to extract the most value from this data. To do this, they need to know the basic approaches to machine learning, including supervised and unsupervised learning. We’ve often mentioned these concepts in our blog, but now it’s time to delve into the details and understand why data annotation is not always the right path in machine learning.
With that said, we’ll try to examine the nuts and bolts of machine learning, such as supervised and unsupervised methods, and explain why annotated data is fundamentally important for one method, while another method doesn’t need it at all. Let’s begin!
What Is the Difference Between Supervised and Unsupervised Learning?
Artificial intelligence can solve a variety of problems, even those that are beyond our control. We know how machine learning can be useful for optimizing the finance or insurance industries, enhancing personalized approaches to customers, or even choosing a good wine for true connoisseurs! So it makes sense to group different models and approaches in ML, depending on the challenge it’s tasked to address. This is why there are families of ML methods, like supervised learning and unsupervised learning, where training data has a different role for each. And the main difference between these approaches to ML is data, namely labeled data.
Before we begin, let’s first recall how machine learning works: ML algorithms are integrated into machines and data streams to extract valuable information and knowledge, which are fed into a system to generate the desired output. For example, it can include prediction or recommendation tasks. These algorithms can be either supervised or unsupervised, or sometimes they fall under the category of reinforcement learning. The latter are the algorithms that learn from data and identify patterns in order to react to an environment. Yet, it’s much more common to distinguish between supervised and unsupervised machine learning.
In essence, the primary difference between these two methods of ML lies in the presence/absence of labels in the training data subset. More specifically, supervised algorithms require the training data to perform analytical tasks before they build contingent functions to map fresh instances of the attribute. The classification and regression algorithms are the two main types of supervised ML.
Unsupervised algorithms, on the other hand, don’t require an annotated training dataset. Instead, this method is based on pattern recognition, with no need for a target attribute. This means that all the variables are used as inputs, which is best suited for clustering and association mining techniques in ML. Unsupervised algorithms can identify inherent groupings within the unlabeled data and then apply labels to each data point. Both supervised and unsupervised learning are extensively employed to complete various data mining tasks, but the choice of an algorithm depends on the requirements of the learning task.
Supervised vs. Unsupervised Classification
Supervised classification models learn by example how to answer a predefined question about each data point. In contrast, unsupervised models are, by nature, exploratory and there’s no right or wrong output. Supervised learning relies on annotated data (manually by humans) and learns from it. The example that the supervised model learns from is the unique cognitive capacity of humans to extract meaning from context, which machines still lack to develop. A training model is used in supervised classification to cluster new inputs into predetermined categories that are applicable during training.
Conversely, there’s no annotated data (aka labels) in an unsupervised classification method. When using this method, you must examine the input to identify the structure of the data, and then classify or cluster it based on the structure. Inputs are fed into the model, but the category of each output is not specified. The training inputs are clustered into groups with similar characteristics.
Therefore, the goal of supervised learning is to predict new data outputs. The type of results is known upfront. In unsupervised learning, however, the purpose is to extract knowledge from huge amounts of data because, in this method, the algorithm identifies the unusual or interesting attributes in the dataset on its own.
What Is Supervised Learning & Why It Needs Data Annotation
Judging by the name of this ML method, there’s obviously something that is being supervised. That is, a machine learning model that is supervised by a data expert. Simply put, you inform the system of the appropriate output for a given input in supervised learning.
Supervised learning relies on the expertise of a data scientist, who trains an ML algorithm with labeled inputs of data to get the desired output. For instance, an image of an animal is labeled as “dog,” so the goal is to train the algorithm so that it can eventually predict the right label for all the images containing a dog. In this case, the algorithm learns from the training dataset to generate accurate predictions iteratively. Yet, even though supervised models are considered more accurate than unsupervised ones, they still require human involvement to annotate the data for the model training.
You can always send your data to our team of annotation pros to make it easier to cope with supervised learning. Contact our team at Label Your Data to ensure that your ML model is fed with secure and accurately labeled data!
Examples of Supervised ML
Some of the most common applications of supervised learning include sentiment analysis, text categorization, face recognition, weather forecasting, price prediction, and spam detection. It’s quite a simple method in machine learning, which is often computed using languages like R or Python. However, the process of training such models may be time-consuming, and it requires robust data annotation expertise to provide the right labels for input and output variables.
Supervised learning is a much more common and more accurate ML method that can tackle complex and tedious tasks. It’s an ideal approach for:
- Binary classification: Trains a model using only two class labels to predict a discrete or categorical target variable.
- Multi-class classification: Applies a large number of different class labels to the object or the data.
- Regression modeling: Tackles the issue of predicting a continuous response variable.
- Ensembling: Uses a variety of models to predict an outcome, including different algorithms or training datasets.
What Is Unsupervised Learning & Where to Apply Unlabeled Data
Unsupervised learning, on the other hand, doesn’t require a data scientist to be involved in the process. No labels or corresponding outputs are needed. Instead, unsupervised ML implies that the algorithm is fed with unlabeled input data to detect patterns that form the groups of data. The algorithm itself analyzes the underlying structure of the data.
Thus, unsupervised models function independently, and they can find the inherent structure of unlabeled data on their own. Of course, they still require human involvement to validate output variables and ensure that the results are satisfactory.
Examples of Unsupervised ML
Unsupervised models also have a broad spectrum of applications in machine learning, including recommendation engines, anomaly detection, customer segmentation, and medical imaging, to name a few. However, since you have a lot of unclassified data to work with, you need sophisticated tools to handle computationally difficult unsupervised models. Besides, unless human intervention is applied to evaluate the output variables, an unsupervised method might produce radically inaccurate results.
Unsupervised ML is an ideal approach for:
- Clustering: Organizes data into segments (groups) and reveals similar patterns in the dataset.
- Segmentation: Seeks to find and locate semantically relevant categories with no need for labeled data.
- Anomaly detection: Detects data points that deviate from the normal behavior.
- Association mining: Employs a variety of rules to uncover correlations between variables in a dataset.
- Dimensionality reduction: Identifies the characteristics of data values that produce the largest variation between data points.
Unsupervised Data Mining
The basic concept of machine learning is to analyze hidden patterns in data, which is the process called data mining. A model then uses these patterns to predict outcomes or classify a given set of data, given the problem posed to the model. In unsupervised data mining, the algorithms have a tendency to discover rules that accurately describe connections between the attributes. This is why unsupervised models are sometimes called descriptive models because they look for unknown patterns in a dataset (unlabeled) with no or minimum human supervision.
These models focus on the data’s fundamental structure, relations, and interconnections rather than predicting a target value. Unsupervised data mining refers to the method of using ML algorithms where there’s no outcome variable that is predicted or classified, like in supervised data mining techniques. The goal of this type of data mining is to explore and analyze patterns in the given dataset based only on the relationships between data points. Therefore, unsupervised learning helps identify unknown (hidden) patterns in data using clustering, association, and extraction. However, unsupervised methods are notoriously slow, and they present a number of scalability concerns.
An unsupervised method of data mining is applied when there’s no specific goal of an ML project or when one aims to discover hidden patterns and relationships within the dataset. While unsupervised methods can do without labeled data, data annotation projects remain an essential element in supervised learning. So don’t hesitate to reach out to our Label Your Data team and get your data handled by the pros!
Supervised or Unsupervised: Choose Your ML Method Wisely
Machine learning is a viable strategy that makes it easier to navigate and eventually succeed in the modern business environment. The only problem is to evaluate your project’s needs and choose the right approach to machine learning.
If you’re working on an ML project, you need to know these basics we’ve discussed in order to understand what solution is the best for you. The success of your ML project lies in your ability to evaluate the structure and volume of your input data, set the right goals for the project, and examine your algorithms’ alternatives. If it so happens that supervised learning is your match, you will need expert support from data annotation companies. Contact our team of annotators at Label Your Data to find the most suitable labeling solution for your current project in AI! We provide a free pilot project, fully customized to your project’s goals.
In supervised learning, classifying large volumes of data might be challenging, but highly accurate and reliable results are well worth the effort. Unsupervised learning, on the other hand, can deal with massive amounts of unlabeled data in real-time. However, if you’ve faced the transparency issue of data clustering or a higher risk of inaccurate output, semi-supervised learning kicks in. But we’ll save this topic for future discussion, so stay tuned!
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