Random forest

Artificial intelligence (AI) is undoubtedly one of the most important technological developments of the last hundred years, with wide-ranging applications and the potential to boost efficacy and efficiency in all kinds of industries and systems.

A random forest is an artificial intelligence algorithm based on machine learning , which can be applied across the board to turn disparate data into predictions for the future. With highly accurate possibilities, the random forest algorithm changes how we engage with data.

What is random forest?

Random forest definition: Random forest is a machine learning algorithm that uses multiple decision trees to make predictions and decisions.

Random forest is a unique machine learning algorithm, which operates differently from other algorithms in artificial intelligence. It builds on decision tree algorithms by using multiple decision trees that work together to arrive at more accurate predictive outcomes or classifications. A decision tree is a supervised learning algorithm designed to classify data and make predictions based on a hierarchical tree-like structure. It is crucial to understand decision trees in order to grasp random forests.

This algorithm is distinct from similar machine learning models and algorithms in that it has a reduced risk of overfitting. Whereas decision trees are limited to tight samples within training data, the large number of decision trees and potential samples within a random forest allows for greater flexibility and more interaction with data.

How does random forest work?

A random forest algorithm brings together multiple decision trees to push data through a more complex classification process. Once done, it arrives at more accurate predictions and decisions.

In a decision tree, data is fed from the root node through internal nodes and on to output leaf nodes. While the root node is the core input data, leaf nodes represent all possible outcomes from the dataset.

Hyperparameters are configuration variables in machine learning that control how a model behaves, so outcomes will be influenced by the setting of hyperparameters. Random forest algorithms have three hyperparameters that need to be set ahead of training:

• Node size: The node size specifies the depth of decision trees.
• Number of decision trees: This hyperparameter denotes how many decision trees will be used within the data processing.
• Number of features sampled: Feature sampling affects accuracy and bias within the decision trees.

Random forests are composed of individual decision trees, each of which has its own data sample from the input data. Each decision tree has only a sample of the training data that is originally input to the model, and the decision trees are kept unique and distinct from one another through feature sampling.

This sampling of the data and use of multiple decision trees to handle different aspects of the same data are what ensures randomness and less similar outcomes than individual decision trees generate. The disparity in outcome is what makes random forest a more accurate, more deeply analytical machine learning algorithm.

What is the history of the random forest algorithm?

The random forest algorithm is a recent technology, though it is rooted in artificial intelligence and deep learning technologies.

• 1990s, randomized decision trees: The general concept of randomized decision trees to build an algorithm was initiated by Steven Salzberg and David Heath in 1993. This concept was developed further by Tin Kam Ho in 1995, with the establishment of feature sampling for enhanced accuracy.
• Mid-1990s, introduction of randomness: Yali Amit and the Geman brothers (Stuart and Donald) contributed significantly to the field of machine learning, which led to the development of random forests and introduced variation among individual decision trees.
• 2000s, codification of concepts into algorithms: In the 2000s, Leo Breiman and Adele Cutler registered Random Forests as a trademark and codified the algorithm with the development of the “bagging” technique, as well as the random feature sampling approach.

What are key types of random forest algorithms?

Random forest algorithms fall into two categories according to what they are aiming to achieve:

• The regressor random forest is used to make predictions based on the input data.
• The classifier random forest is used to make predictions based on classification.

How is random forest used?

As a machine learning algorithm, random forests have a wide range of applications across industries. Some key industries that make innovative use of random forest technology are healthcare , finance, trading and e-commerce.

Random forest algorithms are used to improve patient care. For example, multiple decision trees work through a patient’s potential to respond to a treatment in a specific way, depending on the medical history. Similarly, within pharmaceutical medicine, random forests can help identify the correct combination of components to enhance efficiency and safety.

Within finance industries, banks use random forests to analyze people’s banking histories and credit records to identify which customers are likely and unlikely to pay debts, leading to smarter lending decisions. Within trading, stock traders use random forests to predict how a stock or a market is likely to perform in the future based on historical data.

E-commerce retailers enhance customer experience and satisfaction with random forests, as they can predict shopping habits and recommend products in a more customer-oriented way.