Tag Archives: Boosting Ensemble

How to apply CatBoost Classifier to yeast dataset

How to apply CatBoost Classifier to yeast dataset     CatBoost is a powerful machine learning library that can be used to improve the performance of decision tree models. It is especially useful for datasets with categorical features and is known for its ability to handle missing data and categorical features automatically. In this essay, …

How to apply XGBoost Classifier to yeast dataset

How to apply XGBoost Classifier to yeast dataset XGBoost is a powerful machine learning library that can be used to improve the performance of decision tree models. It is especially useful for large datasets and for datasets with a lot of features. In this essay, we will be discussing how to use the XGBoost library …

How to tune Hyperparameters in Gradient boosting Classifiers in Python

  In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to tune Hyperparameters in Gradient boosting Classifiers in Python.   Tuning the hyperparameters in Gradient Boosting Classifiers is an important step in the machine learning …

How to tune depth parameter in boosting ensemble Classifier in Python

How to tune depth parameter in boosting ensemble Classifier in Python     Tuning the depth parameter in a boosting ensemble classifier is an important step in the machine learning process. It allows us to optimize the performance of the classifier by finding the best value for the depth parameter. In this essay, we will …

How to apply Gradient Boosting Classifier to adult income data

How to apply Gradient Boosting Classifier to adult income data     Gradient Boosting Classifier is an ensemble machine learning algorithm that builds multiple weak models and combines their predictions to improve the overall performance of the model. In this essay, we will be discussing how to apply the Gradient Boosting Classifier to predict adult …

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 do Fashion MNIST image classification using LightGBM in Python

How to do Fashion MNIST image classification using LightGBM 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 LightGBM, a gradient boosting library that is …

How to do Fashion MNIST image classification using GradientBoosting in Python

How to do Fashion MNIST image classification using GradientBoosting 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 Gradient Boosting, a powerful and efficient algorithm …

Data Science Coding | H2O in Python with Grid Search Cross Validation | IRIS Dataset

Data Science Coding | H2O in Python with Grid Search Cross Validation | IRIS Dataset H2O.ai is an open-source platform that provides a wide range of machine learning algorithms and tools for building, deploying, and managing models. It is written in Java and has APIs for several programming languages, including Python. Grid Search Cross Validation …

Data Science Coding | SKLEARN XGBoost Classifier with Grid Search Cross Validation | WACAMLDS

SKLEARN XGBoost Classifier with Grid Search Cross Validation   XGBoost is a powerful and efficient implementation of the Gradient Boosting algorithm that is used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses …