Random Forest Algorithm

What is Tree-based Algorithms?

Tree-based algorithms are a class of supervised machine learning models that construct decision trees to typically partition the feature space into regions, enabling a hierarchical representation of complex relationships between input variables and output labels. Examples of notable are random forests, Gradient Boosting techniques and decision trees, using recursive binary split based on criteria like Gini impurity or information gain etc. These algorithms show versatility in use in classification and regression functions, for robustness against overfitting by ensemble methods and generates more individual trees Ability to allow exploratory analysis of feature importance, which is economical, which contributes to widespread application in various fields like healthcare and natural language processing....

How do Tree-based algorithm work?

The main four workflows of tree-based algorithms are discussed below:...

Decision Tree

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Random Forest Algorithm

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Gradient Boosting Machines

A decision tree is a visual tool used to guide decision-making by considering different conditions. It resembles an inverted tree with branches and leaves pointing downwards. At each branch, a decision is made based on specific criteria, leading to a conclusion at the end of each branch. Decision trees are valuable for structuring decisions and problem-solving processes. At each branch, you make a choice based on certain conditions, and eventually, you reach a conclusion at the end of a branch. Decision trees are commonly used in various fields, such as business, education, and medicine, to help people make choices and solve problems....

XGBoost

Random Forest Algorithm combines the power of multiple decision trees to create robust and accurate predictive models. It works on the principle of group learning, where multiple individual decision trees are built independently in which each is trained on a random subset of data reduces ting and increases the generalizability of the model. When a prediction is needed, each tree in the forest gives its vote, and the algorithm combines these votes together by aggregation to give the final prediction. This tree-based approach not only improves the prediction accuracy but also increases the algorithm’s ability to detect noisy data and outliers....

Other ensemble methods

Gradient Boosting Machine is like a team of little learners that work together to solve a big problem. Each learner starts with some basic knowledge and tries to improve by focusing on the mistakes made by the previous learners. They keep getting better and better at solving the problem until they reach a good solution. This teamwork approach helps Gradient Boosting Machines to tackle complex tasks effectively by combining the strengths of multiple simple learners....

Advantages of Tree-Based Algorithms

eXtreme Gradient Boosting, often abbreviated as XGBoost, is a sophisticated method in computer science for solving problems through learning. The algorithm combines multiple decision trees to make accurate predictions. The model continuously improve from its past mistakes. It can handle a wide range of tasks, such as categorizing data or predicting values, with high precision and efficiency....

Disadvantages of Tree-Based Algorithms

Now we are going discuss other some popular Ensemble methods below:...