Unlocking AI Secrets: Linear Regression's 99% Accuracy in 200 Steps

Linear regression, a foundational algorithm in data science, is pivotal for understanding complex AI models. It predicts the relationship between variables, like house size and price, by drawing a line of best fit through data points. The model uses parameters, which are adjusted during training to minimize prediction errors. A key metric, Mean Square Error (MSE), measures the model’s accuracy by squaring the difference between predicted and actual values. Gradient descent, an optimization algorithm, adjusts these parameters in small steps to find the lowest MSE, balancing progress and precision. In a practical example, predicting sponge prices based on dimensions, linear regression achieved near-perfect accuracy after 200 iterations, demonstrating its effectiveness and the importance of mastering these basics for AI development.

Source: towardsdatascience.com