Sklearn Logistic Regression

Logistic Regression is a linear model for classification and implementation can fit binary, One-vs-Rest, or multinomial logistic regression with optional , or Elastic-Net regularization.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnLogisticRegression

Fields

Name

Title

Description

targetCol

Target Column

The label column for model fitting

featureCols

Feature Columns

Feature columns of type - all numeric, boolean and vector

splitRatio

Split Ratio

Split Ratio

penalty

Penalty

The norm used in the penalization.

dual

Dual

Dual or primal formulation.

tol

Tol

Tolerance for stopping criteria.

C

C

Inverse of regularization strength; must be a positive float.

fit_intercept

Fitintercept

Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.

intercept_scaling

InterceptScaling

Useful only when the solver ‘liblinear’ is used and fit_intercept is set to True.

class_weight

ClassWeight

Weights associated with classes in the form {class_label: weight}.

random_state

RandomState

The seed of the pseudo random number generator to use when shuffling the data.

solver

Solver

Algorithm to use in the optimization problem.

max_iter

Maxiter

Maximum number of iterations taken for the solvers to converge.

multi_class

MultiClass

If the option chosen is ‘ovr’, then a binary problem is fit for each label. For ‘multinomial’ the loss minimised is the multinomial loss fit across the entire probability distribution.

verbose

Verbose

For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.

warm_start

WarmStart

When set to True, reuse the solution of the previous call to fit as initialization.

confusionMatrix

Confusion Matrix

output_confusion_matrix_chart

Output Confusion Matrix Chart

whether to display confusion matrix chart.

cm_chart_title

Confusion Matrix Chart Title

Title name to display in Confusion Matrix Chart

cm_chart_description

Confusion Matrix Chart Description

Description to display in Confusion Matrix CHart

confusionMatrixTargetLegend

Confusion Matrix Target Legend

Legend name to display for Target in Confusion Matrix

confusionMatrixPredictedLabelLegend

Confusion Matrix PredictedLabel Legend

Legend name to display for Predicted Label in Confusion Matrix

confusionMatrixCountLegend

Confusion Matrix Count Legend

Legend name to display for Count in Confusion Matrix

path

Save Confusion Matrix Path

Save Confusion Matrix

Description

Confusion Matrix Description

confusionMatrixRowDescription

Confusion Matrix Outcome description

One can provide the business details of the outcome of the confusion matrix rows

ROC Curve

ROC Curve

output_roc_curve

Output ROC Curve

whether to display confusion matrix chart.

roc_title

ROC Curve Chart Title

Title name to display in ROC Curve Chart

roc_description

ROC Curve Chart Description

Add Description for ROC Curve Chart

xlabel

X Label

X label

ylabel

Y Label

Y Label