There are three main branches of machine learning. One of the most popular is supervised machine learning. Among the most accurate machine learning methods, it is used to predict concise data needed in various industries.
So, what can supervised machine learning do for you, and how does it work? Let's take a closer look below.
Supervised machine learning is a predictive analysis technique used to make predictions based on inputs, also called features.
In simple words, supervised machine learning is where machines are trained to predict output based on well “labeled” training data.
Its input would be a set of examples classified into one of two categories: the positive and the negative. Because it is supervised, it requires labeled data as input using one or more classifiers. The goal of the analysis is to predict the value of a label for new, unlabeled data.
In other words, supervised machine learning processes labeled data and produces statistical estimations for future outcomes.
This can be useful in many applications such as retail, face categorization, spam detection, and more. But we'll get into that later. For now, let's talk about how it works.
Let's keep this simple. A supervised algorithm breaks the data into two parts: the training set and the testing set.
The training set is used to make predictions based on past observations of the data. This may be done by processing the entire dataset, finding features through a mathematical formula, or other methods. Once these features are found and assigned numeric values (also called labels), they're combined to form a majority opinion among them to create an aggregate prediction for future outcomes. The testing set is the real test to see how good the algorithm is at making predictions. If a model can accurately predict a future outcome with this set, it's considered accurate and ready for production.
For instance, let's say you fed in a set of 100 patient records, with one record per patient. You can use the example of sorting them by age, gender, diagnosis, and so on. If you're trying to classify what kind of disease each patient has, you can use the feature to find the best indicators for that specific condition by looking at other variables in each record. You can then create a formula that places this data point into proper categories to look for future results.
While many different applications use supervised machine learning, below are the most common examples.
Supervised machine learning is used in retail to predict customers' purchasing behavior. It can predict future sales based on previous purchases, using relevant features such as the time of day, the type of store they are in, their income level, or other demographic information. This can help with inventory levels and staffing decisions.
Finance also uses supervised machine learning for predictions. These include predicting stock market volatility based on past trends performed during volatile periods, even for specific stocks over a longer period. Financial institutions also use supervised machine learning for fraud detection and anti-money laundering compliance.
Supervised learning can also help predict whether or not a patient will die from heart failure based on history and treatment plan. More recent applications also include cancer cell detection, where machine learning algorithms are used to sort cancerous cells from non-cancerous ones.
Another application of supervised machine learning is signature recognition, which is used for fraud detection and criminal investigations. By acquiring a person's precise handwriting style, features can identify documents written by the same person.
This works by zeroing in on parts of a writing sample that are repeated, as well as the different features of the writing style. These features are then matched with new samples submitted for classification to look for matches based on those signatures.
Supervised machine learning is similarly used in spam detection to filter out unauthorized senders hiding behind fake addresses. This is done by monitoring targets, which are the senders' addresses, for changes over time. These features can act as indicators of legitimate email behavior, allowing users to determine whether or not their email is spam.
The use of supervised machine learning for weather forecasting helps predict changes in the weather through the use of historical data. Predictions are based on past patterns found in similar weather patterns to determine what will happen next. For example, the past 24 hours of weather data can be used to determine the weather the next day.
Supervised machine learning is also used in image classification, which compares images instead of text to compare similarities between them. Similar images are placed in the same category, and new ones are categorized according to the rules set for this model. This is useful for separating similar images that may contain more details that distinguish them from each other. It is effective even when the images may have been taken at the same times and from the same angles.
Face recognition technology uses supervised machine learning to identify people in photos or videos based on their facial features. Different algorithms can be applied using classified features (e.g., hair color, eye color, smile, etc.) for accurate face matching. These can then be matched against full-length photos of people to make determinations for features.
Supervised machine learning is used to predict the side effects of new medications, how they may interact with other medications, and to determine when an overdose has occurred. This is achieved by looking at the chemical structure of the drug and potential receptor interactions with other drugs or compounds that affect that same receptor.
Supervised machine learning is a form of machine learning increasingly used in various industries and professions. It has become a popular area of research and development for people who can use these predictions to make informed decisions with greater accuracy.
Software like Graphite focuses on giving you the ability to leverage technology like this without knowing a single line of code. Get started on harnessing the full potential of your data today by getting in touch with us.