AI Algorithm Explorer

Decision Trees

Decision trees are machine learning models that classify or predict by splitting data into subgroups based on input features, forming a tree-like structure.

K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a simple machine learning algorithm that classifies data points based on the majority class of their k closest neighbors in the feature space.

Linear Regression

Linear regression predicts outcomes by finding the best-fitting straight line through data points, minimizing the difference between predicted and actual values.

Support Vector Machines (SVM)

Support Vector Machines find an optimal hyperplane to separate data classes, maximizing the margin between different categories in high-dimensional space.

Random Forest

Random Forest is a machine learning algorithm that combines multiple decision trees to create a robust and accurate predictive model for classification and regression tasks.

Naive Bayes

Naive Bayes is a probabilistic machine learning algorithm that uses Bayes' theorem to make predictions, assuming independence between features.

A perceptron is a simple neural network that learns to classify inputs into two categories. It consists of input nodes, weights, a bias, and an activation function. Perceptrons are the building blocks of more complex neural networks.

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AI Algorithm Explorer

Decision Trees

K-Nearest Neighbors (KNN)

Linear Regression

Support Vector Machines (SVM)

Random Forest

Naive Bayes

Perceptron

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Graph Neural Networks (GNNs)

Deep Convolutional Neural Networks (DCNNs)

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