Contents
What objective function do we use in logistic regression?
Logistic regression transforms its output using the logistic sigmoid function to return a probability value.
Why is a logistic model better?
Logistic regression is a simple and more efficient method for binary and linear classification problems. It is a classification model, which is very easy to realize and achieves very good performance with linearly separable classes. It is an extensively employed algorithm for classification in industry.
Which is better Adam or Nadam?
With the Fashion MNIST dataset, Adam/Nadam eventually performs better than RMSProp and Momentum/Nesterov Accelerated Gradient. This depends on the model, usually, Nadam outperforms Adam but sometimes RMSProp gives the best performance.
Can a loss function be used in logistic regression?
Linear regression uses Least Squared Error as loss function that gives a convex graph and then we can complete the optimization by finding its vertex as global minimum. However, it’s not an option for logistic regression anymore.
How to optimize a logistic regression in Python?
We can still apply Gradient Descent as the optimization algorithm. It takes partial derivative of J with respect to θ (the slope of J), and updates θ via each iteration with a selected learning rate α until the Gradient Descent has converged. See the python query below for optimizing L2 regularized logistic regression.
How to regularize logistic regression regularized with optimization?
We used special optimization function in lieu of gradient descent to get the optimal values of the coefficients. The data sets are from the Coursera machine learning course offered by Andrew Ng. The course is offered with Matlab/Octave. I am doing the exercises in that course with R.
When to use Class 0 in logistic regression?
For logistic regression, focusing on binary classification here, we have class 0 and class 1. To compare with the target, we want to constrain predictions to some values between 0 and 1.