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Optimizing Databricks Workloads: Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads Agile Machine Learning with DataRobot: Automate each step of the machine learning life cycle, from understanding problems to delivering value Bennett and M.

An optimization per- sion 0. Jiang and H. Breiman, J. Friedman, R. Olshen, and C. Khoussainov, X. Zuo, and N. Grid- Stone. Classification and Regression Trees. Celis and D. Weka-parallel: machine learning in parallel. Technical report, Carleton College, [19] M. Krogel and S. Facets of aggregation ap- CS TR, Horvath and A. Yamamoto, editors, Work-in-Progress Track at the [5] C. Chang and C. Software available at Programming ILP , Mierswa, M.

Wurst, R. Klinkenberg, M. Scholz, and [6] T. Dietterich, R. Lathrop, and T. Yale: Rapid prototyping for complex data Solving the multiple instance problem with axis-parallel mining tasks. Ungar, M. Craven, D. Gunopu- rectangles. Dietzsch, N. Gehlenborg, and K. Mayday- Knowledge discovery and data mining, pages —, a microarray data analysis workbench.

Dong, E. Frank, and S. Ensembles of bal- [21] D. Balie—baseline information extraction : anced nested dichotomies for multi-class problems.

In Multilingual information extraction from text with ma- Proc 9th European Conference on Principles and Prac- chine learning and natural language techniques. Springer, Fan, K. Chang, C. Hsieh, X. Wang, service award.

Journal of Machine Learning. Research, —, R Foundation for [10] E. Frank and S. Ensembles of nested di- Statistical Computing, Vienna, Austria, ISBN 3- chotomies for multi-class problems. In Proc 21st In- ACM Press, Rodriguez, L. Kuncheva, and C. Ro- tation forest: A new classifier ensemble method.

IEEE [11] R. Gaizauskas, H. Cunningham, Y. Wilks, P. Cross-validation 5. Other estimates 5. Comparing data mining schemes 5. Predicting probabilities 5. Counting the cost 5. Evaluating numeric prediction 5. Minimum description length principle 5. Applying the MDL principle to clustering 5. Implementations 6.

Decision trees 6. Classification rules 6. Association rules 6. Extending linear models 6. Instance-based learning 6. Numeric prediction with local linear models 6. Bayesian networks 6. Clustering 6. Semisupervised learning 6. Multi-instance learning 6. Data Transformations 7. Attribute selection 7. Discretizing numeric attributes 7. Projections 7. Sampling 7. Cleansing 7. Transforming multiple classes to binary ones 7.

Calibrating class probabilities 7. Further reading 7. Ensemble Learning 8. Combining multiple models 8. Bagging 8. Randomization 8. Boosting 8. Additive regression 8. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projectsOffers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methodsIncludes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface.

Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization.