Logistic Regression
The logistic regression is a classification method, to predict the probability of the class of the categorical depended variable. The categorical dependent variable can be a binary or multiclass.
For example, in a binary classification algorithm, the logistic regression predict the probability of a categorical dependent variable where data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.). In other words, the logistic regression model predicts P(Y=1) as a function of X.
Logistic Regression Assumptions
For example, in a binary classification algorithm, the logistic regression predict the probability of a categorical dependent variable where data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.). In other words, the logistic regression model predicts P(Y=1) as a function of X.
Logistic Regression Assumptions
- Binary logistic regression requires the dependent variable to be binary.
- The independent variables should be independent of each other. That is, the model should have little or no multicollinearity.
- The independent variables are linearly related to the log odds.
- Logistic regression requires quite large sample sizes.