Logistic regression is preferrable over a simpler statistical test such as chi-squared test or Fisher’s exact test as it can incorporate more than one explanatory variable and deals with possible ...

The S-form curve is called the Sigmoid function or the logistic function. In logistic regression, we use the concept of the threshold value, which defines the probability of either 0 or 1. Such as ...

Logistic regression is a powerful technique for binary classification, which means assigning data points to one of two possible categories, such as yes or no, spam or not spam, or positive or ...

In this project, I implement Logistic Regression algorithm with Python. I build a classifier to predict whether or not it will rain tomorrow in Australia by training a binary classification model ...

There are many different algorithms that can be used to train a multi-class logistic regression model and each algorithm has several variations. Common algorithms include stochastic gradient descent ...

When training a logistic regression model, there are many optimization algorithms that can be used, such as stochastic gradient descent (SGD), iterated Newton-Raphson, Nelder-Mead and L-BFGS. This ...

There is no assumption of normal distribution for the independent variables in logistic regression. In addition to the regression equation, the report includes odds ratios, confidence limits, ...