Supervised Learning vs. Unsupervised Learning: Which is Better?

Supervised and unsupervised learning are two types of machine learning algorithms that are commonly used in artificial intelligence (AI). The main difference between these two types of learning is the amount of input and output data that is provided to the algorithm during training. The result is being able to leverage data for different circumstances and applications. 

Supervised Learning in Artificial intelligence

Supervised learning is a method of training a machine learning algorithm by providing the model with a labeled dataset. The dataset consists of input data, also known as features, and corresponding output data, also known as labels. The algorithm is trained to find patterns in the input data that can be used to predict the output data. This is done by adjusting the parameters of the algorithm until it is able to correctly predict the output for a given input. Some examples of supervised learning algorithms include linear regression, logistic regression, and decision trees.

One of the main benefits of supervised learning is that it can be used to make predictions and classify new input data. This is because the algorithm has been trained on a labeled dataset, which means it knows the correct output for a given input. This makes supervised learning a popular choice for tasks such as image classification, speech recognition, and natural language processing.

Supervised learning involves two separate problems when data mining:  classification and regression:

  • Classification problems use an algorithm to assign data into certain categories. An example would be a supervised learning algorithm used to classify spam in a separate folder from your inbox.
  • Regression is another learning method that uses an algorithm to delineate relationships between dependent and independent variables. These can be used for predicting things like sales revenue projections. 

Unsupervised Learning in Artificial Intelligence

Unsupervised learning is a machine learning strategy using algorithms and unlabeled datasets. The algorithm is then trained to seek out patterns in the input data without any corresponding output data to go off of initially. It groups similar inputs together and identifies the underlying structure of the data. 

Unsupervised learning is typically used for exploratory data analysis and data visualization. This is because the algorithm does not have any corresponding output data, which means it cannot be used to make predictions or classify new input data. Instead, it can be used to identify patterns and structure in the data, which can be beneficial to gain insights and understanding of the data.

Unsupervised learning is typically used for three common situations: clustering, association, and dimensionality reduction:

  • Clustering is a method for grouping unlabeled data based on their similarities or differences. 
  • Association uses different rules to find relationships between variables in a given dataset. Association is often used in situations to understand market analysis and recommendations for things like “Customers Who Bought This Item Also Bought” recommendations.
  • Dimensionality reduction is a learning technique used when the number of features  (or dimensions) in a given dataset is too large. It reduces the number of data inputs to a manageable size yet preserves the integrity of the data. 

Data Needed for Learning

Supervised learning algorithms require a large amount of labeled data to train on, while unsupervised learning algorithms can often be trained using smaller datasets. This is because supervised learning algorithms need to learn the correct output for a given input, thus requiring a large amount of labeled data. Conversely, unsupervised learning algorithms only need to find patterns in the input data, which can often be done with smaller datasets. 

Artificial Intelligence Learning Applications

Each type of artificial intelligence learning can be helpful, but each has unique circumstances where they are ideal to employ. Supervised learning is typically used for making predictions and classifying new input data, while unsupervised learning is largely used for exploratory data analysis and data visualization. Both have their own advantages and disadvantages, and the choice of which type of learning to use depends on the task and the amount of available data. 

At Sentiero, we’ve seen our companies use both types of artificial intelligence learning to their advantage. For growing startups, knowing when to leverage each can help tremendously and allow a company to provide some amazing insights into data.