Supervised Machine Learning
What is Supervised Machine Learning?
Supervised machine learning algorithms uncover insights, patterns, and relationships from a labeled training dataset – that is, a dataset that already contains a known value for the target variable for each record. Because you provide the machine learning algorithm with the correct answers for a problem during training, the algorithm is able to “learn” how the rest of the features relate to the target, enabling you to uncover insights and make predictions about future outcomes based on historical data.
Examples of Supervised Machine Learning Techniques
- Regression, in which the algorithm returns a numerical target for each example, such as how much revenue will be generated from a new marketing campaign.
- Classification, in which the algorithm attempts to label each example by choosing between two or more different classes. Choosing between two classes is called binary classification, such as determining whether or not someone will default on a loan. Choosing between more than two classes is referred to as multiclass classification.
Why is Supervised Machine Learning Important?
Supervised machine learning turns data into real, actionable insights. It enables organizations to use data to understand and prevent unwanted outcomes or boost desired outcomes for their target variable.
Supervised Machine Learning Use Cases
Supervised machine learning is one of the most powerful engines that enable AI systems to make business decisions faster and more accurately than humans. Businesses across industries use it to solve problems such as:
- Reducing customer churn
- Determining customer lifetime value
- Personalizing product recommendations
- Allocating human resources
- Forecasting sales
- Forecasting supply and demand
- Detecting fraud
- Predicting equipment maintenance
Challenges in Implementing Supervised Machine Learning
However, successfully building, scaling, and deploying accurate supervised machine learning models has historically required extensive time and technical expertise from a team of highly skilled, expensive data scientists. Additionally, data science teams must periodically rebuild models in order to make sure the insights they provide remain true to life as the input data changes.
Supervised Machine Learning + DataRobot
DataRobot’s diverse library of machine learning algorithms and unique model blueprint technology incorporates supervised machine learning algorithms such as bagging, boosting, deep learning, frequency-severity methods, generalized additive models, generalized linear models, kernel-based methods, random forests, and many others. Additionally, DataRobot’s team of experienced data scientists is constantly researching, developing, and testing new open-source algorithms, ensuring the platform incorporates the most state-of-the-art supervised machine learning models.