CatBoost

How to compare boosting ensemble Classifiers in Python

How to compare boosting ensemble Classifiers in Python   Boosting ensemble classifiers are a powerful machine learning technique that can be used to improve the performance of a wide range of classification tasks. These classifiers work by combining the predictions of multiple weak models to produce a more accurate final prediction. In this essay, we …

How to apply CatBoost Classifier to adult income data

How to apply CatBoost Classifier to adult income dataset     CatBoost Classifier is a powerful ensemble machine learning algorithm that is specifically designed to handle categorical features, which are features that take on a limited number of discrete values. It is an open-source library developed by Yandex, and it is built on top of …

Image classification using CatBoost: An example in Python using CIFAR10 Dataset

Image classification using CatBoost: An example in Python using CIFAR10 Dataset     Image classification is a task of assigning a label to an image based on its visual content. It is a fundamental problem in the field of computer vision and has many practical applications, such as self-driving cars and image search engines. One …

How to do Fashion MNIST image classification using CatBoost in Python

How to do Fashion MNIST image classification using CatBoost in Python     Fashion MNIST is a dataset of images of clothing items, such as shirts, pants, and sneakers, with the goal of training models to recognize and classify them. One popular method for image classification is using CatBoost, a gradient boosting library that is …

How to find optimal parameters for CatBoost using GridSearchCV for Classification in Python

How to find optimal parameters for CatBoost using GridSearchCV for Classification in Python To find the optimal parameters for CatBoost using GridSearchCV for Classification in Python, you can follow these steps: Define the CatBoostClassifier model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …

How to find optimal parameters for CatBoost using GridSearchCV for Regression in Python

How to find optimal parameters for CatBoost using GridSearchCV for Regression in Python To find the optimal parameters for CatBoost using GridSearchCV for Regression in Python, you can follow these steps: Define the CatBoost model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …

How to classify “wine” using different Boosting Ensemble models e.g. XgBoost, CatBoost, LightGBM – Multiclass Classification in Python

How to classify “wine” using different Boosting Ensemble models e.g. XgBoost, CatBoost, LightGBM – Multiclass Classification in Python Boosting is a popular machine learning technique that is often used to improve the performance of a classifier. A boosting algorithm combines the predictions of multiple simpler models to make a more accurate final prediction. In this …

How to use CatBoost Classifier and Regressor in Python

How to use CatBoost Classifier and Regressor in Python CatBoost is an open-source gradient boosting library that is particularly good at handling categorical variables, making it ideal for datasets with many categorical features. It is used for both classification and regression problems. In this article, we will go over the basics of how to use …