pyspark.ml.classification.
LogisticRegressionTrainingSummary
Abstraction for multinomial Logistic Regression Training results.
New in version 2.0.0.
Methods
fMeasureByLabel([beta])
fMeasureByLabel
Returns f-measure for each label (category).
weightedFMeasure([beta])
weightedFMeasure
Returns weighted averaged f-measure.
Attributes
accuracy
Returns accuracy.
falsePositiveRateByLabel
Returns false positive rate for each label (category).
featuresCol
Field in “predictions” which gives the features of each instance as a vector.
labelCol
Field in “predictions” which gives the true label of each instance.
labels
Returns the sequence of labels in ascending order.
objectiveHistory
Objective function (scaled loss + regularization) at each iteration.
precisionByLabel
Returns precision for each label (category).
predictionCol
Field in “predictions” which gives the prediction of each class.
predictions
Dataframe outputted by the model’s transform method.
probabilityCol
Field in “predictions” which gives the probability of each class as a vector.
recallByLabel
Returns recall for each label (category).
totalIterations
Number of training iterations until termination.
truePositiveRateByLabel
Returns true positive rate for each label (category).
weightCol
Field in “predictions” which gives the weight of each instance as a vector.
weightedFalsePositiveRate
Returns weighted false positive rate.
weightedPrecision
Returns weighted averaged precision.
weightedRecall
Returns weighted averaged recall.
weightedTruePositiveRate
Returns weighted true positive rate.
Methods Documentation
New in version 3.1.0.
Attributes Documentation
Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.
Notes
In most cases, it will be values {0.0, 1.0, …, numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.
Objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.
Returns weighted averaged recall. (equals to precision, recall and f-measure)
Returns weighted true positive rate. (equals to precision, recall and f-measure)