Instructions for Use

Logistic regression is a widely used classification machine learning algorithm used to estimate the likelihood of event occurrence. Although the name of logistic regression carries the word 'regression', it is actually a classification method mainly used for binary classification problems, where there are only two outputs representing two categories. For example, we want to predict whether a patient will recover, whether customers will purchase products, and so on. This method is used for the data training process of logistic regression, which can obtain models based on data features and be used for prediction. Return to the summary of the logistic regression model: *IRCharacteristics: The attributes of a logistic regression model. *Variable: An array of field names in a logistic regression model, referring to the fields of the independent variables in the training model. *Mse: Mean square error, the mean squared error between the predicted value and the true value. *RMSE: Root mean square error, the mean square root of the error between the predicted value and the true value. *Mae: mean absolute error, the mean value of the absolute value of the error between the predicted value and the true value. *R2: Determination coefficient. Based on the value of r2, the quality of the model can be judged, with a value range of [0,1]. Generally speaking, a larger r2 indicates a better fitting effect of the model. R2 reflects approximately how accurate it is, because as the number of samples increases, r2 will inevitably increase, and cannot truly quantitatively explain the accuracy level. It can only be roughly quantified. *Explained Variance: Explained variance. *NumIterations: The actual number of iterations.

Parameter Description