If you want to hop on the artificial intelligence bandwagon, it’s time to understand random forest algorithms and random forest modeling. Random forest is a regulated form of the machine learning algorithm. It’s one of the most common algorithms because of its pristine accuracy and user-friendly characteristics. It’s often used for regression tasks as well as classification duties. Subsequently, random forest makes it easy to monitor and track a wide range of data situations.
To understand random forest, it’s important to be familiar with decision trees which are basically the foundations of the random forest model. According to TIBCO, decision trees are something that you most likely use every day in your life. Just like an actual forest with real trees, a random forest is a group of decision trees—hence the name. Already, you’re able to get a clear vision of how this data model works.
Decision trees are support tools that use a tree-like design of data decisions and their possible outcomes. A decision tree is useful for separating data using their own specific features often through visual aids such as numbers and varying colors. Decision trees in random forest are one of the easiest algorithms to display different data and their consequences. Keep reading to learn more about when to use random forest.
The random forest is a supervised learning algorithm. Supervised learning is the simplest of all the machine learning methods and, therefore, easier than unsupervised learning, reinforcement learning, and deep learning. Therefore, if the problem you are trying to solve requires a highly sophisticated algorithm, then a random forest classifier is not what you need.
Random forest is also a form of ensemble modeling. Ensemble modeling relies on the majority vote principle—wisdom in numbers. That is why random forest modeling uses a large number of trees rather than a single decision tree.
Ensemble modeling is important when you need clean data. You get clean data by maintaining a low bias and a low variance. Your data gets dirty when you have a high bias and/or a high variance and/or when noise enters your dataset. Therefore, if scrubbing up your data so that it’s sparkly and clean, without a high bias or a high variance, then a random forest algorithm is a good fit for you. However, if you like your data raw and dirty and having a high variance and/or high bias is not that big a deal, then using a single decision tree might be suit you better.
Decision Tree Algorithms
Decision tree algorithms are perfect for busy companies. Random forest algorithms rely heavily on decision trees so if your business depends on decision trees, then random forest algorithms are perfect for you.
Random forest is well suited to basic regression problems and basic classification problems. For a regression task, random forest actually makes use of regression trees. The random forest algorithm is also useful as one of the many types of classification algorithms. If you are trying to solve regression problems or classification problems, then random forest is the most optimal algorithm for you.
When random forest is used to solve regression problems, it’s a form of linear regression, which is a relatively simple form of regression. If you need a more sophisticated regression model, logistic regression is the model for you.
Linear regression is good for making accurate predictions, with a relatively high degree of prediction accuracy, provided that you use a random forest algorithm with bootstrap aggregation—a.k.a. bagged trees. However, if you want to map out actual probabilities, linear regression will not suffice. For that, you need a logistic regression model, preferably in Python.