How gradient descent method is used for Minimising the cost function in linear regression?
Minimizing the cost function: Gradient descent Gradient descent enables a model to learn the gradient or direction that the model should take in order to reduce errors (differences between actual y and predicted y). At this point the model has optimized the weights such that they minimize the cost function.
Does Sklearn linear regression use gradient descent?
The scikit-learn has two approaches to linear regression: To obtain linear regression you choose loss to be L2 and penalty also to none or L2 (Ridge regression). There is no “typical gradient descent” because it is rarely used in practice.
Does gradient descent always converge for convex function?
Gradient Descent need not always converge at global minimum. It all depends on following conditions; If the line segment between any two points on the graph of the function lies above or on the graph then it is convex function.
What are some examples of linear regression?
Okun’s law in macroeconomics is an example of the simple linear regression. Here the dependent variable (GDP growth) is presumed to be in a linear relationship with the changes in the unemployment rate. In statistics, simple linear regression is a linear regression model with a single explanatory variable.
What is gradient descent?
Gradient descent. Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.
What is Batch Gradient descent?
(Batch) gradient descent algorithm. Gradient descent is an optimization algorithm that works by efficiently searching the parameter space, intercept() and slope() for linear regression, according to the following rule: