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.