What is Random Forest?
Random Forest is a supervised machine learning algorithm. It has become one of the most commonly used algorithms due to its accuracy, simplicity, and flexibility. The fact that it can be used for both classification and regression tasks, combined with its non-linear nature, makes it highly adaptable to a wide variety of data and situations.
The term "random decision forest" was first proposed by He Tianqin in 1995. He Tianqin developed a formula for creating forecasts using random data. Then in 2006, Leo Breiman and Adele Cutler extended the algorithm to create what we know today as random forests. That means the technology and the math and science it utilizes is still relatively new.
It's called a "forest" because it generates a forest of decision trees. Data from these trees are then combined to ensure the most accurate predictions. While individual decision trees have only one outcome and narrowly scoped groups, forests ensure more groups and decisions, resulting in more accurate results. It also has the benefit of adding randomness to the model by finding the best features in a random subset of features. Collectively, these strengths create a model with a wide variety that is favored by many data scientists .
What is a decision tree?
A decision tree is something you've probably used every day of your life. It's like asking a friend for advice on what sofa to buy. Your friends will ask you what is important to you. size? color? Fabric or Leather? Based on these decisions, you can find the perfect sofa according to your choice. A decision tree basically asks a series of right or wrong questions that lead to a specific answer.
Each "test" (leather or fabric?) is called a node. Each branch represents the result of that selection (fabric). Each leaf node is a label for that decision. Obviously, in the real case, it splits the observations so that the entire group is different, producing subgroups that are similar to each other but different from other groups.
Difference Between Decision Tree and Random Forest
A random forest is a collection of decision trees. However, there are some differences between the two. Decision trees often create rules that are used to make decisions. Random Forest will randomly select features and make observations, build a decision tree, and then calculate the average result.
In theory, a large number of uncorrelated decision trees would produce more accurate predictions than a single decision tree. This is because a large number of decision trees work together to protect each other from individual errors and overfitting.
In order for random forests to perform well, three things are required:
- An identifiable signal so the model doesn't just guess
- Predictions made by a tree must have low correlation with other trees
- Features with some degree of predictive power: GI=GO
How Do Businesses Use the Random Forest Algorithm?
In a business setting, there are many applications for random forests. For example, a single decision tree might classify a data set related to wine, classifying various wines as light or strong wines.
Random forests create many trees, which complicates the prediction of the final outcome. It can take wine information and generate lots of trees comparing price, tannin, acidity, alcohol content, sugar, availability, and various other characteristics. Then, the results are averaged and it predicts the (arguably) best overall wine based on a large number of criteria.
In the enterprise, the random forest algorithm can be used in scenarios where there is a range of input data and complex situations. For example, determine when a customer leaves the company. Churn is complex and often involves a range of factors: product cost, satisfaction with the final product, customer support efficiency, ease of payment, contract length, extra features offered, and demographics such as gender, age, and location. The Random Forest algorithm creates decision trees for all these factors and can accurately predict which of an organization's customers are at high risk of churn.
Another complex example is trying to predict which customers will spend the most in a year. A comprehensive set of variables and attributes are analyzed and it is possible to predict the marketing department's target audience for the year.
Fill in the decision forest
Filling (also known as bootstrapped aggregation) allows a single decision tree to randomly sample from a dataset and replace the data, resulting in vastly different outcomes within a single decision tree. This means that each tree fetches only part of the data, not all available data. These individual trees then make decisions based on the data they have and predict outcomes based on only those data points.
This means that in each random forest, there are trees trained on different data and using different features in order to make decisions. This provides buffers for the trees, protecting them from errors and incorrect predictions.
The filling process only uses about two-thirds of the data, so the remaining one-third can be used as a test set.
Benefits of Random Forests
Easy to measure relative importance
Measuring the importance of a feature is as simple as looking at the nodes that use that feature to reduce impurity for all trees in the forest. The difference before and after permuting a variable is easy to see, which is a measure of the importance of that variable.
multipurpose
Because random forests can be used for both classification and regression tasks, they are very versatile. It easily handles binary and numerical features as well as categorical features without conversion or rescaling. Unlike almost all other models, it is very efficient on all types of data.
no overfitting
As long as there are enough trees in the forest, there is little risk of overfitting. Decision trees can also end up overfitting. Random forests prevent this by building trees of different sizes from subsets and merging the results.
high accuracy
Using many trees that differ significantly between subgroups can make random forests a highly accurate predictive tool.
Reduce time spent on data management
In traditional data processing, a large portion of precious time is spent cleaning data. Random forests work well with missing data to minimize this occurrence. Tests comparing predictions derived from complete and incomplete data show nearly identical levels of performance. Peripheral data and non-linear features are essentially obsolete.
Random forest techniques can also balance errors in population and other imbalanced datasets. It does this by minimizing the error rate, so larger classes will have a lower error rate and smaller classes will have a higher error rate.
fast training speed
Because random forests use subsets of features, they can quickly evaluate hundreds of different features. This means that the prediction speed is also faster than other models, because the resulting forest can be saved and reused in the future.
Random Forest Challenge
The result is slower
Since the algorithm is building many trees, it increases the complexity and accuracy of predictions. However, it slows down the process because it's building hundreds or thousands of trees. This makes it ineffective for real-time forecasting.
Solution: You can use out-of-bag (OOB) sampling, ie use only two-thirds of the data for prediction. The random forest process is also parallelizable, so the process can be split across many machines and run much faster than on a single system.
Can't infer
Random forest predictions rely on the average of previously observed labels. Its range is bounded by the lowest and highest labels in the training data. While this is only a problem in scenarios where the training and prediction inputs have different ranges and distributions, this covariate shift is a problem and means that different models should be used in some cases.
low interpretability
The random forest model is the ultimate black box. They cannot be explained, making it difficult to understand how or why they came to a certain decision. This impenetrability means that models must be trusted as-is and results accepted as-is.
Alternatives to Random Forests
neural network (NN)
Neural networks are algorithms that work together to identify relationships in data. It's designed to try to replicate the way the human brain works, always changing and adjusting to incoming data. It has a significant advantage over random forests because it can handle data other than tabular formats, such as audio and images. It can also be fine-tuned using many hyperparameters to suit the desired data and results.
However, if the data you're working with is tabular only, it's best to stick with random forests as it's simpler and still produces good results. Neural networks can be labor and computer intensive, and for many computations the fine-grained details may not be needed at all. For simple tabular data, neural networks and random forests perform similarly in prediction.
Extreme Gradient Boosting (xGBoost)
Extreme Gradient Boosting is said to be more accurate and more powerful than Random Forest. It combines Random Forest and Gradient Boosting (GBM) to create a more accurate set of results. xGBoost takes slower steps, making predictions sequentially rather than independently. It uses the patterns in the residuals, strengthening the model. This means that the prediction error is smaller than the random forest prediction.
linear model
Linear predictive models are one of the simplest machine learning techniques. They are widely used and, when performed on the right dataset, are powerful predictive tools. They are also easy to interpret and don't have black box effects like random forests. However, because they only use linear data, the agility is significantly lower than random forests. If the data is non-linear, Random Forest will produce the best predictions.
cluster model
The top five clustering methods include fuzzy clustering, density-based clustering, partition methods, model-based clustering, and hierarchical clustering. They all form similar groups or clusters by clustering a group of objects together in some form. It is a technique used in many areas of data science as part of data mining , pattern recognition, and machine learning. While clustering can be used in random forests, it is a technique in its own right.
Clustering models are excellent at adapting to new examples, generalizing cluster size and shape, and the results provide valuable data insights.
However, clustering does not handle outliers and non-Gaussian distributions well. Clustering can have scaling problems when dealing with large numbers of samples. Finally, the number of features may be large, even greater than the number of samples.
Support Vector Machine (SVM)
Support vector machines analyze data and then use it for regression analysis and classification. It is a reliable predictive method that can reliably build models that classify data points. These models rely on the notion of distance between points, although this may not make sense in all cases. While a random forest gives you the probability of belonging to a class in a classification problem, a support vector machine gives you the distance to the boundary, so it still needs to be transformed to make it a probability.
Bayesian network
A Bayesian network is a graphical model that shows variables, dependencies, and probabilities. They are used to build models from data, predict outcomes, detect anomalies, provide inference, run diagnostics, and assist in decision making. Bayesian networks are generative and can model the probability distribution of a given random variable. It is best suited for complex queries on random variables.
Random forests are descriptive models, often used for classification. If you're interested in causality, then Bayesian networks may be a better fit than random forests. If the data pool is large, random forests are better.
The Future of Random Forests
Efficient, adaptable, and agile, random forests are the supervised machine learning model of choice for many data scientists. It offers a range of benefits not found in many alternatives and provides accurate predictions and classifications. However, this is largely unexplained and can be a bit of a black box in terms of how the results were achieved.
In the future, combining classical random forests with other strategies may lead to more accurate predictions and further refine the results. Furthermore, the leap towards explainable machine learning is now becoming a reality, which may help unlock some of the mysteries of random forest prediction.