📄️ Artificial Neural Network Classification
ANN classification refers to the use of Artificial Neural Networks (ANNs) for solving classification problems. ANNs are computational models inspired by the biological neural networks in the human brain. They consist of interconnected nodes called artificial neurons or units, organized into layers.
📄️ Naive Bayes Classification
Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features. Despite its simplistic assumptions, Naive Bayes is a popular and effective algorithm for text classification tasks, such as spam filtering or sentiment analysis.
📄️ Random Forest Classification
Random Forest is a popular ensemble machine learning algorithm used for classification tasks. It is based on the concept of decision trees and combines the predictions of multiple decision trees to make a final classification decision. Random Forest is known for its robustness, accuracy, and ability to handle high-dimensional data.
📄️ SVM Classification
Support Vector Machine (SVM) is a popular supervised machine learning algorithm used for classification tasks. It works by finding the optimal hyperplane that separates different classes of data points in the feature space.
📄️ Gradient Boosting Classification
Gradient boosting classification is a machine learning algorithm that uses a series of weak learners to create a strong classifier. The weak learners are trained sequentially, with each learner being trained to correct the errors made by the previous learners. This process is repeated until the desired level of accuracy is achieved.