Random forest

Random Forest Bagging Illustration

Random Forest is a versatile machine learning algorithm capable of performing both regression and classification tasks. It also undertakes dimensional reduction methods, treats missing values, outlier values, and other essential steps of data exploration, and does a fairly good job. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model. In the case of Random Forest, the weak models are decision trees. Random Forests correct for decision trees' habit of overfitting to their training set.

Overview

Random Forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. The fundamental concept behind random forest is a simple but powerful one — the wisdom of crowds. In data science speak, the reason that the Random Forest model works so well is:

• A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models.

The uncorrelated models are created by using bagging and feature randomness when building trees to try to create an uncorrelated forest of trees. The algorithm for inducing a random forest was developed by Leo Breiman and Adele Cutler, and "Random Forests" is their trademark. The term came from random decision forests that were first proposed by Tin Kam Ho of Bell Labs in 1995.

Algorithm

The process of creating a random forest algorithm includes the following steps:

1. Start with the selection of random samples from a given dataset.
2. Construct a decision tree for each sample and get a prediction result from each decision tree.
3. Perform a vote for each predicted result.
4. Select the prediction result with the most votes as the final prediction.

Advantages

Random Forest has several advantages:

• It can be used for both classification and regression tasks.
• It can handle a large dataset with higher dimensionality. It can handle thousands of input variables without variable deletion.
• It gives estimates of what variables are important in the classification.
• It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing.

Disadvantages

Despite its versatility, Random Forest also has some disadvantages:

• It can be complex and require more computational resources, thereby increasing the time to train the model.
• It can also overfit datasets that are particularly noisy.
• For data including categorical variables with different numbers of levels, random forests are biased in favor of those attributes with more levels. Therefore, variable importance scores from random forest are not reliable for this type of data.

Applications

Random Forest is used in a variety of applications such as in banking, stock market, medicine, and e-commerce. In banking, it is used for the detection of customers more likely to repay their debt on time, or use the bank's services more frequently. In finance, it is used to detect stock market behavior and the expected loss or profit. In medicine, it can be used for identifying the correct combination of components in medicine and to analyze a patient's medical history to identify diseases. In e-commerce, Random Forest is used to recommend products based on customer likes and dislikes.

See Also

• Machine Learning
• Decision Trees
• Ensemble Learning
• Overfitting
• Regression
• Classification

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