Classification AI Algorithms
There are many Classification AI algorithms. Some popular ones are:
Naïve Bayesian
classification is a supervised machine learning algorithm used for classification tasks by modeling input class distribution on the assumption that the presence of a particular feature in a class is independent of the presence of any other feature. Its (over)simplicity only requires a linear number of parameters to the number of variables in a learning process - hence only takes linear learning time.
Decision Trees
are a type of supervised learning algorithm for both classification and regression tasks. They work by recursively partitioning the input space into smaller regions based on the values of the input features.
Support Vector Machines (SVM)
is a supervised learning algorithm to define the hyperplane that best separates the input data into different classes for classification and regression tasks.
Logistic Regression
is a statistical method that can be used for binary (YES or NO) classification tasks.
K-Nearest Neighbors (KNN)
classification is a non-parametric algorithm that finds the k-nearest neighbors of the input data in the feature space and uses their labels to derive the label for classification and regression tasks.
Random Forests
classification combines multiple decision trees to improve the accuracy and robustness of the classification model.Classification models may achieve higher accuracy levels when coupled with
