Formally a decision tree is a graphical representation of all possible solutions to a decision. These days, tree-based algorithms are the most commonly used algorithms in case of supervised learning scenarios. They are easier to interpret and visualize with great adaptability. We can use tree-based algorithms for both regression and classification problems, However, most of the time they are used for classification problem. Let's understand a decision tree from an example: Yesterday evening, I skipped dinner at my usual time because I was busy taking care of some stuff. Later in the night, I felt butterflies in my stomach.
Most techniques of predictive analytics have their origins in probability or statistical theory (see my post on Naïve Bayes, for example). In this post I'll look at one that has more a commonplace origin: the way in which humans make decisions. When making decisions, we typically identify the options available and then evaluate them based on criteria that are important to us. The intuitive appeal of such a procedure is in no small measure due to the fact that it can be easily explained through a visual. The tree structure depicted here provides a neat, easy-to-follow description of the issue under consideration and its resolution.
Classification is a two-step process, learning step and prediction step, in machine learning. In the learning step, the model is developed based on given training data. In the prediction step, the model is used to predict the response for given data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. Decision Tree algorithm belongs to the family of supervised learning algorithms.
Trees have long been a subject of interest and a topic of discussion -- and it's no wonder; they represent life, growth, peace, and nature. Trees provide us with many benefits necessary for survival, including clean air, filtered water, shade, and food. They also give us hope and insight, and courage to persevere -- even in the harshest conditions. Trees teach us to stay rooted while soaring to great heights. As we know the tree has been useful to us in many different forms, but in the recent times, its structure has given us inspiration for an algorithm to solve problems and make a machine learn things we want them to learn.
By Clare Liu, Data Scientist at fintech industry, based in HK. A decision tree is one of the popular and powerful machine learning algorithms that I have learned. It is a non-parametric supervised learning method that can be used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. For a classification model, the target values are discrete in nature, whereas, for a regression model, the target values are represented by continuous values.