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Obtaining an indication of confidence of predictions is desirable for many data mining applications. Such confidence levels, together with the predicted value, can inform on the certainty or extent of reliability that may be associated with the prediction. This can be useful, for example, where model outputs are used in making potentially costly decisions, and one may then focus on the higher confidence predictions, and in general across risk sensitive applications. The conformal prediction framework presents a novel approach for complementing predictions from machine learning algorithms with valid confidence measures. Confidence levels are obtained from the underlying algorithm, using a non-conformity measure which indicates how 'atypical' a given example set is. The non-conformity measure is key to determining the usefulness and efficiency of the approach. This paper considers inductive conformal prediction in the context of random tree ensembles like random forests, which have been noted to perform favorably across problems. Focusing on classification tasks, and considering realistic data contexts including class imbalance, we develop non-conformity measures for assessing the confidence of predicted class labels from random forests. We examine the performance of these measures on multiple datasets. Results demonstrate the usefulness and validity of the measures, their relative differences, and highlight the effectiveness of conformal prediction random forests for obtaining predictions with associated confidence.