What machine learning algorithms can you use to automate decision-making?

1 Supervised learning

Supervised learning is a type of machine learning algorithm that learns from labeled data, meaning that each input has a corresponding output or target. Supervised learning algorithms can be used to automate decision-making tasks such as classification or regression, where you need to assign a category or a numerical value to a new input, based on the patterns learned from the training data. For example, you can use supervised learning algorithms to automate decisions such as whether to approve a loan application, based on the applicant's credit history, income, and other features, or how much to charge for a product, based on the demand, supply, and other factors.

2 Unsupervised learning

Unsupervised learning is a type of machine learning algorithm that learns from unlabeled data, meaning that there is no predefined output or target for each input. Unsupervised learning algorithms can be used to automate decision-making tasks such as clustering or dimensionality reduction, where you need to group or simplify the data based on some similarity or difference criteria. For example, you can use unsupervised learning algorithms to automate decisions such as how to segment your customers into different groups, based on their behavior, preferences, and characteristics, or how to reduce the complexity of your data, by extracting the most relevant features or components.

3 Reinforcement learning

Reinforcement learning is a type of machine learning algorithm that learns from its own actions and feedback, meaning that there is no fixed data set or target, but rather a dynamic environment and a reward system. Reinforcement learning algorithms can be used to automate decision-making tasks such as optimization or control, where you need to find the best action or policy to maximize the reward or minimize the cost, in a given situation. For example, you can use reinforcement learning algorithms to automate decisions such as how to play a game, based on the rules, the state, and the outcome, or how to control a robot, based on the sensors, the actions, and the goals.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?