What algorithm curate machine learning

In order to address a specific problem, practitioners must select an acceptable learning algorithm. A general rule of thumb is that for classification issues, we should use algorithms with high accuracy, whereas for regression problems, we should choose algorithms with lower accuracy but higher robustness because the absolute error rate is unimportant. Here are a few examples: Linear Regression: Linear regression uses the linearity principle to predict continuous values from a set of input variables. It achieves this by minimizing the total of squared errors. This method is fast and scalable for huge data sets since it avoids iterating over all possible replies; nonetheless, it is unstable.