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.
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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