org.apache.spark.ml.classification
LogisticRegressionTrainingSummary
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package classification
org.apache.spark.ml.classification
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the true label of each instance (if available).
Field in "predictions" which gives the true label of each instance (if available).
objective function (scaled loss + regularization) at each iteration.
objective function (scaled loss + regularization) at each iteration.
Field in "predictions" which gives the prediction of each class.
Field in "predictions" which gives the prediction of each class.
Dataframe output by the model's transform method.
Dataframe output by the model's transform method.
Field in "predictions" which gives the probability of each class as a vector.
Field in "predictions" which gives the probability of each class as a vector.
Returns accuracy.
Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
Convenient method for casting to binary logistic regression summary.
Convenient method for casting to binary logistic regression summary. This method will throws an Exception if the summary is not a binary summary.
Returns f1-measure for each label (category).
Returns f1-measure for each label (category).
Returns f-measure for each label (category).
Returns f-measure for each label (category).
Returns false positive rate for each label (category).
Returns false positive rate for each label (category).
Returns the sequence of labels in ascending order.
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.
Note: 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.
Returns precision for each label (category).
Returns precision for each label (category).
Returns recall for each label (category).
Returns recall for each label (category).
Number of training iterations.
Number of training iterations.
Returns true positive rate for each label (category).
Returns true positive rate for each label (category).
Returns weighted averaged f1-measure.
Returns weighted averaged f1-measure.
Returns weighted averaged f-measure.
Returns weighted averaged f-measure.
Returns weighted false positive rate.
Returns weighted false positive rate.
Returns weighted averaged precision.
Returns weighted averaged precision.
Returns weighted averaged recall.
Returns weighted averaged recall. (equals to precision, recall and f-measure)
Returns weighted true positive rate.
Returns weighted true positive rate. (equals to precision, recall and f-measure)
:: Experimental :: Abstraction for multiclass logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.