Types Of Problems Solved Using AI Algorithms

- Overview

Here are some types of AI models: 

• Linear regression: Uses a simple mathematical function to map input data to output data using a linear relationship. 
• Decision tree: A supervised ML algorithm that can be used to make predictions or decisions based on specific input data.  Decision trees are especially useful for problems that involve multiple variables. 
• Deep neural networks: A popular AI/ML model that uses layers of artificial neurons to combine multiple inputs and provide a single output value.  The design for this deep learning model is inspired by the human brain and its neural network. 
• Naive Bayes: A simple yet effective AI model that is based on the Bayes Theorem and is especially applied for test classification. 
• Random forest: An AI model where each decision tree returns its own result or decision. 
• Support vector machine (SVM): A common AI algorithm that can be used for either classification or regression. SVM works by plotting each piece of data on a chart. 
• Linear Discriminant Analysis (LDA): A subsection of logistic regression that is most frequently used when more than two values need to be defined in the output. 
• Generative AI: A type of artificial intelligence that creates models that can generate new data or content similar to what it has been trained on. 

When choosing an AI model, you can consider factors such as: 

• Problem categorization
• Model performance
• Explanability of the model
• Model complexity
• Data set type and size
• Feature dimensionality
• Training duration and expense
• Inference speed