Logistic Regression Model is a generalized form of Linear Regression Model. It is a very good Discrimination Tool. Following are the advantages and disadvantage of Logistic Regression:

**Advantages of Logistic Regression****1.**Logistic Regression performs well when the**dataset is linearly separable**.

**2.**Logistic regression is less prone to over-fitting but it can overfit in high dimensional datasets. You should consider Regularization (L1 and L2) techniques to avoid over-fitting in these scenarios.**3.**Logistic Regression not only gives a measure of how relevant a predictor (coefficient size) is, but also its direction of association (positive or negative).**4.**Logistic regression is easier to implement, interpret and very efficient to train.**Disadvantages of Logistic Regression****1.**Main limitation of Logistic Regression is the**assumption of linearity**between the dependent variable and the independent variables. In the real world, the data is rarely linearly separable. Most of the time data would be a jumbled mess.**2.**If the number of observations are lesser than the number of features, Logistic Regression should not be used, otherwise it may lead to overfit.**3.**Logistic Regression can only be**used to predict discrete functions**. Therefore, the dependent variable of Logistic Regression is restricted to the discrete number set. This restriction itself is problematic, as it is prohibitive to the prediction of continuous data.
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