1. Machine Learning for SQL Use Cases
  2. Concepts
  3. Machine Learning Algorithms
  4. About Generalized Linear Model
  5. Logistic Regression
  6. Row Diagnostics for Logistic Regression

Row Diagnostics for Logistic Regression

GLM provides detailed row diagnostics for logistic regression, offering insights into individual predictions.

For logistic regression, the diagnostics table has the columns described in the following table. All the columns are NUMBER, except the CASE_ID and TARGET_VALUE columns, which preserve the type from the training data.

Table 7-14 Row Diagnostics Table for Logistic Regression

Column Description

CASE_ID

Value of the case ID column

TARGET_VALUE

Value of the target value

TARGET_VALUE_PROB

Probability associated with the target value

HAT

Value of the diagonal element of the hat matrix

WORKING_RESIDUAL

Residual with respect to the adjusted dependent variable

PEARSON_RESIDUAL

The raw residual scaled by the estimated standard deviation of the target

DEVIANCE_RESIDUAL

Contribution to the overall goodness of fit of the model

C

Confidence interval displacement diagnostic

CBAR

Confidence interval displacement diagnostic

DIFDEV

Change in the deviance due to deleting an individual observation

DIFCHISQ

Change in the Pearson chi-square