Data mining and machine learning algorithms are trained on large datasets to find useful hidden patterns. These patterns can help to gain new insights and make accurate predictions. Usually, the training data is structured in a tabular format, where the rows represent the training instances and the columns represent the features of these instances. The feature values are usually real numbers and/or categories. As very large volumes of digital data are becoming available in many domains, the data is often summarized into manageable sizes for efficient handling. To aggregate data into histograms is one means to reduce the size of the data. However, traditional machine learning algorithms have a limited ability to learn from such data, and this thesis explores extensions of the algorithms to allow for more effective learning from histogram data.

The thesis focuses on the decision tree and random forest algorithms, which are easy to understand and implement. Although, a single decision tree may not result in the highest predictive performance, one of its benefits is that it often allows for easy interpretation. By combining many such diverse trees into a random forest, the performance can be greatly enhanced, however at the cost of reduced interpretability. By first finding out how to effectively train a single decision tree from histogram data, these findings could be carried over to building robust random forests from such data. The overarching research question for the thesis is: How can the random forest algorithm be improved to learn more effectively from histogram data, and how can the resulting models be interpreted? An experimental approach was taken, under the positivist paradigm, in order to answer the question. The thesis investigates how the standard decision tree and random forest algorithms can be adapted to make them learn more accurate models from histogram data. Experimental evaluations of the proposed changes were carried out on both real world data and synthetically generated experimental data. The real world data was taken from the automotive domain, concerning the operation and maintenance of heavy-duty trucks. Component failure prediction models were built from the operational data of a large fleet of trucks, where the information about their operation over many years have been summarized as histograms. The experimental results showed that the proposed approaches were more effective than the original algorithms, which treat bins of histograms as separate features. The thesis also contributes towards the interpretability of random forests by evaluating an interactive visual tool for assisting users to understand the reasons behind the output of the models.