Use of Decision Trees and Random Forest in Machine Learning. An Insight into Supervised Learning for Classification Problems
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The 3 main categories of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In this post, we shall focus on supervised learning for classification problems.
Supervised learning learns from past data and applies the learning to present data to predict future events. In the context of classification problems, the input data is labeled or tagged as the right answer to enable accurate predictions.
This is how decision trees are combined to make a random forest. In this article, I describe how this can be used for a classification task with the popular Iris dataset.
Decision Tree is one of the most widely used machine learning algorithm. It is a supervised learning algorithm that can perform both classification and regression operations.
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