Logistic Regression Details

The Model Detail screen includes the following information for Logistic Regression models:

Metrics

Provides training, test, and n-fold data for the following:

• Mean squared error (MSE)
• Root mean squared error (RMSE)
• Number of observations
• R-squared (R2)
• Logarithmic loss (Logloss)
• Area under the curve (AUC)
• Precision-recall area under the curve (PR AUC)
• Gini coefficient
• Mean per class error
• Akaike information criterion (AIC)
• Lambda
• Residual deviance
• Null deviance
• Null degree of freedom
• Residual degree of freedom

Maximum Metrics Threshold

Provides the Training Maximum Metrics Threshold for training, test, and n-fold data using the following metrics:

• max f1
• max f2
• max f0point5
• max accuracy
• max precision
• max recall
• max specificity
• max absolute_mcc
• max min_per_class_accuracy
• max mean_per_class_accuracy

Confusion Matrix

Illustrates the performance of a model on a set of training, test, and n-fold data for which the true values are known.

Standardized Coefficient Chart

Shows the most important predictors by providing the relative value of the coefficients, which indicates how much a change in input changes the objective.

GLM Coefficients

Shows coefficients for a Generalized Linear Model, which estimates regression models for outcomes following exponential distributions.

AUC Curves

Area under the curve; determines which of the used models predicts the classes best using training, test, and n-fold data.

Lift/Gain Curves

Evaluate the prediction ability of a binary classification model using training, test, and n-fold data.