Top 3 Predictive Models in AI

The following are the top three predictive models in AI:

Linear Regression: One of the simplest and most commonly utilized predictive models. It's used to predict a continuous outcome variable based on one or more predictor variables. It works well for problems where the relationship between the variables is linear.

Decision Trees: Versatile and utilized for both classification and regression tasks. They work by splitting the data into subsets based on feature values, creating a tree-like structure of decisions. Decision trees are easy to interpret and can capture non-linear relationships.

Neural Networks: More complex models that are effective for tasks involving large amounts of data and complex patterns. Neural networks consist of layers of interconnected nodes (neurons) and can model highly intricate relationships in the data.

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