Logistic Regression

Logistic regression uses a sigmoid function to perform binary classification, i.e. when you can linearly separate two classes of data from each other.
July 10, 2023
1
min read
Logistic Regression

Advantages

  1. Logistic Regression is one of the simplest machine learning algorithms and is easy to implement, yet it provides great training efficiency in some cases. Also due to these reasons, training a model with this algorithm doesn't require high computation power.
  2. Logistic Regression works well for cases where the dataset is linearly separable: A dataset is said to be linearly separable if it is possible to draw a straight line that can separate the two classes of data from each other. Logistic regression is used when your Y variable can take only two values, and if the data is linearly separable, this algorithm is an efficient way to classify it into two separate classes.

Assumptions

  1. Binary logistic regression requires the dependent variable to be categorical and binary.
  2. Logistic regression requires the observations to be independent of each other. In other words, the observations should not come from repeated measurements or matched data.
  3. Logistic regression requires there to be little or no multicollinearity among the independent variables. This means that the independent variables should not be too highly correlated with each other.