What is the difference between a random forest and a decision tree?
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In the world of artificial intelligence and machine learning Decision trees as well as random forests are well-known algorithms that are used to classify as well as regression work. Although they share a common basis, these models have their distinct characteristics as well as advantages and disadvantages. Knowing the distinction between random forests and the decision tree is vital in deciding on the best model that meets the particular needs of the task or data set. This article examines the primary distinctions between these two algorithms, with a focus on their design, mechanism performance, and the areas of application. Data Science Course in Pune
Introduction to Decision Trees
Decision trees are diagram-like structures in which every internal node is a "test" on an attribute Each branch represents the result from the testing, and every leaf node is a class label (decision made after computing the attributes). The pathways from the roots to the leaves represent the rules of classification.
The appeal of decision trees is their simplicity and comprehensibility. They resemble human decision-making processes, which makes them easy to comprehend and visualize. However, they tend to overfit, especially when working with complex data sets. Overfitting occurs when the algorithm learns to handle the training data too well, and captures noise and the patterns that are underlying, which decreases the ability of the model to adapt to data that is not seen.
Introduction to Random Forests
Random forests however is an ensemble learning method that builds on the ease of decision trees, to overcome their tendency to overfit. It is comprised of a vast variety of decision trees, which work as a group. Each tree within the random forest will spit out a prediction for the class and the class that has the highest number of votes is its model's prediction.
The main reason for the effectiveness of random forests is the variety among the individual trees. This is accomplished through two key principles which are bagging (or bootstrap aggregate) as well as feature randomness. Bagging involves training every tree with an individual sample of data. Feature randomness adds more variety by deciding on the random features that are split at every node. This makes sure that the trees are not correlated and reduces the variability of the model. This leads to improved performance when working with unstudied data.