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Why use logistic regression and why not a linear regression for a classification problem?
Linear regression is suitable for predicting output that is continuous value, such as predicting the price of a property. Whereas logistic regression is for classification problems, which predicts a probability range between 0 to 1. For example, predict whether a customer will make a purchase or not.
Is logistic regression mainly used for regression or classification?
2) True-False: Is Logistic regression mainly used for Regression? Logistic regression is a classification algorithm, don’t confuse with the name regression.
What’s the difference between logit and logistic regression?
One choice of is the logit function. Its inverse, which is an activation function, is the logistic function. Thus logit regression is simply the GLM when describing it in terms of its link function, and logistic regression describes the GLM in terms of its activation function.
How is logistic regression used in the study?
Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. Logistic regression has become an important tool in the discipline of machine learning. The approach allows an algorithm being used in a machine learning application to classify incoming data based on historical data.
What are the disadvantages of logistic regression?
the model will have little to
Why do logistic regression use a sigmoid function?
Logistic Regression is used for Binary classification problem . Sigmoid function is used for this algorithm. However, Sigmoid function is same as linear equation . It divides into classes via threshold in probability outcome. The main advantage is here that we can set threshold as per business requirement.