Defining Cost Function in Machine Learning

A cost function measures the difference between the predicted and actual values in a machine learning model. In Linear Regression, the Mean Squared Error (MSE) is commonly used as the cost function to assess model performance. The cost function helps minimize error and optimize model weights using algorithms like Gradient Descent. The function’s convex shape allows easy identification of the global minima, improving prediction accuracy. Reducing this error leads to better model performance in making accurate predictions.

For more details, check out the full article: Cost function in Logistic Regression in Machine Learning.