Here you’ll find everything about decision trees, whether it’s Hands-on tutorials on how to implement them with Python or conceptual articles.
A decision tree is a type of machine learning algorithm that is used for both regression and classification tasks. It is called a “decision tree” because it creates a tree-like model of decisions that it makes based on the input data. A decision tree works by splitting the input data into smaller and smaller subsets, based on the values of the input features. At each split, the algorithm chooses the feature and the value that produce the purest subset of data. This process continues until the subsets are pure enough, or until the tree reaches a predefined maximum depth. In Python, decision trees are easy to implement, since various Python packages include functions for decision trees: for example Scikit-Learn or Pytorch.
As we enter an era where intelligent systems are increasingly relied upon to make key decisions, responsible AI has become more critical than ever before. It’s not enough to simply rely on data-driven decision-making; we must also ensure that these systems are fair and just. But how can we do this when the same bias … Read more
In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working with a dataset from Kaggle that contains information on 39 different types of crimes, including everything from vehicle theft to drug offenses. Using Python and the powerful Scikit-Learn library, we’ll train a classification model using … Read more
If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the airport trying to catch a flight, only to find out that your flight is delayed. Wasn’t it great to know when a flight will be delayed in advance? We can use past flight delay data … Read more
