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Decision Trees

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.

Building Fair Machine Machine Learning Models with Python and Fairlearn: Step-by-Step Towards More Responsible AI

March 19, 2023March 10, 2023
fairness machine learning unbiased ai responsible ai python tutorial relataly midjourney-min

As we enter an era where intelligent systems are increasingly relied upon to make key decisions, responsible AI has become … Read more

Predictive Policing: Preventing Crime in San Francisco using XGBoost and Python

February 27, 2023March 7, 2021
san francisco crime map python machine learning crime type prediction-min

In this tutorial, we’ll be using machine learning to predict and map out crime in San Francisco. We’ll be working … Read more

Flight Delay Prediction using Azure Machine Learning

December 26, 2022October 15, 2019
flight delay prediction azure machine learning

If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the … Read more

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