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.)
Computes the area under the receiver operating characteristic (ROC) curve.
Computes the area under the receiver operating characteristic (ROC) curve.
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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 a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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 the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
Returns precision for each label (category).
Returns precision for each label (category).
Returns a dataframe with two fields (threshold, precision) curve.
Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
Returns recall for each label (category).
Returns recall for each label (category).
Returns a dataframe with two fields (threshold, recall) curve.
Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic
This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol
.
This will change in later Spark versions.
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 binary logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.