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Random Forest

What is Random Forest?

Random forest is a supervised machine learning algorithm widely used in Classification and Regression problems. Rather than generating a single classification or regression tree, Distributed Random forest generates a forest of classification or regression trees. Each of them generates a type based on a set of attributes. This H2O tree can be considered a vote, and the highest number of votes determines the classification.

Examples of Random Forest

Random forest is used for both classification and regression to determine whether an email is spam.

Besides that, Data scientists use random forests in many industries, including banking, stock trading, medicine, and e-commerce. The information is used to predict things that help these industries run smoothly, such as customer activity, patient history, and safety.

In banking, random forest is used to detect customers who are more likely to repay their debts on time. The data can also predict who will use a bank's services more frequently. It can even be used to detect fraud.

Stock traders use random forests to predict stock price movements. Retail companies use it to recommend products and predict customer satisfaction.

Researchers in China used random forests to study the spontaneous combustion patterns of coal and reduce safety risks in coal mines.

Lastly, random forests are used to analyze medical records to identify diseases in healthcare. Pharmaceutical scientists use random forests to predict drug sensitivity or identify the correct combination of components in a medication. Random forest can even be used for computational biology and genetics.

Why is Random Forest important?

Random forest is one of the most powerful algorithms in Machine Learning. It uses Ensemble Learning (bagging). Random forest adds additional randomness to the model while growing trees. When splitting a node, it searches for the best feature among a random subset of features instead of looking for the most important feature. 

Thus, it reduces the overfitting problem in decision trees and lessens the variance, improving accuracy.

The following are the advantages of random forest:

  1. It can be used to solve both classification and regression problems.
  2. Random forests can handle categorical and continuous variables equally well.
  3. It can automatically handle missing values.
  4. Random forest can handle missing values automatically.
  5. Random forest can handle outliers automatically.
  6. The algorithm is very stable. If a new data point is introduced in the dataset, the overall algorithm is not impacted much since the new data may affect only one tree, but it is improbable to affect all trees.
  7. Random forest is comparatively less affected by noise.

Random Forest vs. Other Technologies & Methodologies

Random Forest vs. Decision Tree

The fundamental difference between a random forest algorithm and a Decision Tree is that Decision Trees are graphs that illustrate all possible outcomes of a decision using a branching approach. In contrast, the random forest algorithm produces a set of decision trees based on the output.

Overfitting and inaccurate results are possible with Decision Trees, but the opposite is true for random forests.

Lastly, Decision Trees are more straightforward to understand, interpret, and visualize than random forests.

Random Forest vs. Xgboost

Xgboost (eXtreme Gradient Boosting) provides machine learning algorithms using the gradient boosting framework.

It supports major operating systems, including Linux, Windows, and macOS. It can run on a single machine or in a distributed environment with frameworks such as Apache Hadoop, Apache Spark, Apache Flink, Dask, and DataFlow.

The library supports a variety of programming languages, including C++, Python, Java, R, Julia, Perl, and Scala.

Random forest is an ensemble learning algorithm that constructs many decision trees during training. For classification tasks, it predicts the mode of the classes, and for regression tasks, it indicates the mean of the trees.

Random subspace and bagging are used during tree construction and have built-in feature importance.

Boosting is an iterative method for learning where the model initially makes a prediction and then analyzes its mistakes and gives more weight to the incorrect data points. In contrast, a random forest is simply a collection of trees in which each tree gives a prediction. At the end of the process, we collect the projections from all the trees and use the mean, median, or mode of this collection as our prediction, depending on the data type (continuous or categorical).

Random Forest vs. Logistic Regression

Generally, logistic regression is better when the number of noise variables is less than or equal to the number of explanatory variables. Random forest is better as the number of explanatory variables increases. In addition, a logistic regression model is based on a path analysis approach, which uses a generalized linear equation to describe the directed dependencies of a set of variables and a random forest top-down induction model to classify and predict. Combining many decision trees (CARTs).

Random Forest vs. Bagging

Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset and then combines all the models' predictions. Random forest is an extension of bagging in which the subsets of features in every data sample are selected randomly. The fundamental difference is that in random forests, only a subset of features is chosen randomly. The best split feature from the subset is used to split each node in a tree, unlike bagging, in which all features are considered to split each node.

Besides that, Bagging improves the accuracy and stability of Machine Learning models using ensemble learning. It reduces the complexity of overfitting models, whereas random forest’s algorithm is robust against overfitting and is good with unbalanced and missing data.