Supervised Machine Learning: Models & Techniques

Supervised machine learning allows an organization to delve into the details of its data, and it reveals important insights, such as customer behavior, fraud and sales trends. Over time, the models can be trained to make correlations between events that a human data analytics team might not think to check.

In this article, we’ll look at how supervised machine learning can help to create predictive AI models that allow management teams to make decisions based on data, not speculation.

What is Supervised Learning?

In its most basic form, supervised machine learning is a machine learning style that uses training data that is labeled and in pairs of input and output data. The model makes predictions on data that it hasn’t even seen before, which is highly accurate when the training is done correctly.

Labeled data is important to this process as it allows for supervised machine learning to make connections by recognizing patterns and relationships within the data, so it becomes more accurate as the model continues to train. The better the labeling, the better the quality of the model at the end of the process.

In the real world, there are examples where supervised machine learning has been used with great success. The two types of models that we mainly see with supervised machine learning techniques are regression and classification models, which we will look at in more detail below.

Regression Techniques

Regression techniques use two different types of data, which are referred to as dependent and independent variables. These are used to help generate regression models. This is useful for analyzing data, such as property prices and crypto market prices. In the case of a property valuation, we would use the property value as the dependent variable, and aspects of the property, such as the size, location and zoning as independent variables.

There are also different subtypes of regression techniques:

• Linear Regression. This is the simplest to explain. It uses a straight line to display all the information points being analyzed, making it good at displaying and predicting trends, much like a graph.

• Polynomial Regression. This method uses polynomial expressions to display complex relationships between variables that wouldn’t fit properly into a straight line.

• Support Vector Regression. This is used when noisy or disorganized datasets are being used. It uses mathematical tools to reduce the errors between what was predicted and what was really the outcome to create accurate models from complicated datasets.

Classification Techniques

Classification models are used in marketing and finance businesses where customers are targeted based on information, such as consumers’ credit scores or spending behavior. If specific campaigns and deals are applicable to these customers, the systems that use the Classification model data can autonomously offer custom-made deals the customer is likely to want.

Another area where classification models are used is in the medical field, where medical conditions and diseases can be predicted by analyzing medical records and histories, combined with test results. This leads to early interventions and results in positive patient outcomes.

Information security and cybersecurity is also an area where classification techniques have been very useful. They allow for suspicious activities to be identified and prevent things like data breaches. This is done by classifying phishing emails, intrusions and even malware.

Decision Trees

Decision trees are useful tools that are used in both regression and classification techniques. They allow researchers and trainers to understand the complicated relationship structures within the training data. They are called decision trees because of the way that the actions branch out based on the choices that the model makes based on the data it is given.

An example where a decision tree could be used is for a climate control application. Weather conditions, the current temperature and how many people are in a building would all affect the temperature and energy consumption of the building. Based on the real-time readings, the decision tree would allow the model to make the best decisions to conserve energy.

Building and Tuning Models

To start building and training models, preprocessing and data clean up needs to happen first. The learning algorithm needs this data to be cleaned up so that it can train properly without additional noise, eventually making it more effective and accurate. If there are missing records in the data, a process called imputation is used. Imputation will make an educated guess, estimate any missing data and add substitutes to the data set.

Outliers, which are discrepancies in the data, are also identified during this data cleaning stage, and they are worked on so that they do not add additional, unwarranted weight to those values which will train the model negatively. Next, feature scaling and transformation processes are applied to the data, allowing the data to be normalized to the point where all the features in the dataset are on par with one another.

The data is then divided into the training set and the testing set so that the overall performance of the model can be evaluated and compared to others. The training set is used to build the model, and the testing set is used to evaluate the results. There are different split ratios involved, for example 80/20 or 70/30.

Finally, cross validation techniques are used to test combinations of data sets and help researchers benchmark and test their models. The result is a predictive model that can be used in an AI application to help classify and predict data.

Predictive models are a game changer in business. When harnessed correctly, predictive models help organizations make accurate decisions with real-time insights from their data. To do that, data teams need to understand the pros and cons of supervised learning techniques, and what each algorithm can do to a particular data set.

The pace at which companies are beginning to adopt these technologies will continue to increase as more services come online to help even smaller organizations get to grips with what their data is trying to tell them. In the end, having a predictive model for businesses will become a necessity, and not a tool that is accessible to only the giant organizations with the biggest AI budgets.

Getting Started with AI and Machine Learning

Microsoft Azure AI Fundamentals (AI-900) covers many of the concepts we’ve discussed here, and it is ideal for IT professionals who want to get started with machine learning and artificial intelligence concepts related to Microsoft Azure services.

Start preparing for the AI-900 today with the CBT Nuggets Machine Learning & AI-900 online training course. This entry-level Microsoft Certified: Azure AI Fundamentals (AI-900) training prepares junior Azure data admins to deploy, configure and use machine learning, artificial intelligence and other bleeding-edge technologies from Microsoft Azure.

Not a CBT Nuggets subscriber? Sign up now! Your first 7 days are free!