10 docs tagged with "classification"

CatBoost is an efficient gradient boosting algorithm that handles categorical features well, making it a powerful tool for classification and regression problems.

In this post, we'll explore the Decision Tree Algorithm, a popular machine learning model used for classification and regression tasks.

In this post, we’ll explore the Extra Trees Algorithm, an ensemble learning model used for classification and regression tasks, known for its efficiency and randomness in both feature selection and data sampling.

In this post, we'll explore the k-Nearest Neighbors (k-NN) Algorithm, one of the simplest and most intuitive algorithms in machine learning.

In this post, we'll explore the Logistic Regression Algorithm, a widely used classification model in machine learning.

In this post, we’ll explore the Naive Bayes Theorem, a fundamental probabilistic algorithm used for classification tasks based on Bayes' Theorem and the assumption of conditional independence.

In this post, we’ll dive into the Random Forest Algorithm, an ensemble learning model used for classification and regression tasks, known for its robustness and versatility.

SVM is a powerful machine learning model known for its effectiveness in classification tasks and its ability to handle high-dimensional data.

This post explores Support Vector Machines (SVM), a powerful classification algorithm that finds the optimal hyperplane to separate different classes in high-dimensional datasets.

XGBoost is a highly efficient and scalable machine learning algorithm known for its accuracy and speed in solving both classification and regression problems.