In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.
In gradient boosting, predictions are made from an ensemble of weak learners. Unlike a random forest that creates a decision tree for each sample, in gradient boosting, trees are created one after the other. Previous trees in the model are not altered. Results from the previous tree are used to improve the next one. In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.
CatBoost is a depth-wise gradient boosting library developed by Yandex. It uses oblivious decision trees to grow a balanced tree. The same features are used to make left and right splits for each level of the tree.
As compared to classic trees, the oblivious trees are more efficient to implement on CPU and are simple to fit.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
What is neuron analysis of a machine? Learn machine learning by designing Robotics algorithm. Click here for best machine learning course models with AI
Python For Machine Learning | Machine Learning With Python, you will be working on an end-to-end case study to understand different stages in the Machine Learning (ML) life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this an ML model will be built on the dataset to get predictions. You will learn about the basics of scikit-learn library to implement the machine learning algorithm.