1. BOAT - Optimistic Decision Tree Construction Gehrke, J. Ganti V., Ramakrishnan R., Loh, W.

2. Problem • Efficient construction of decision trees • As few passes of the database as possible • Sample of dataset to give insight to the full database

3. Motivation • Standard decision tree construction is not sufficient • For tree of height h, need h passes through entire database • To include new data, must rebuild the tree • For large databases, this is not feasible • Need fast, scalable method

4. Intuition • Begin with sample of data • Build decision tree on the sample • For numeric data, use a confidence interval for split • Make limited passes of full data to both verify sampled tree and construct full tree • Only data that falls in confidence interval needs be rescanned to determine how to propagate

5. Criteria selection • Use of impurity functions to generate the attribute to split on • Entropy, gini, index of correlation • Calculated for sample, could be wrong in the full dataset • Minimize the impurity function in attribute selection and confidence interval

6. Confidence Interval • Construct T trees • If at node n, the splitting attribute is not the same in all trees, discard n and its subtree in all trees • For categorical data, if the splitting subset is not identical in all trees, remove node n and its subtree in all trees

7. Confidence Interval • Confidence interval on numeric attributes determined by the range of split points on the T trees • Exact split point is likely to be between the min and max of the values of the split points on the T trees.

8. Verification • Verifying predictions • Use a lower bound for the impurity function to determine if confidence interval and splitting attribute are correct • Discard node and its subtree completely if incorrect • Rerun algorithm on any set of data related to a discarded node

9. Invalidated Predictions • Discarded top nodes would result in resampling of entire database • No savings on full scans • Doesn’t usually happen • Basic probability distribution likely captured by sample • Error in the detail (low) level

10. Dynamic Environments • No need to frequently rebuild the decision trees • Store the confidence intervals • Only need rebuild of tree if underlying probability distribution changes

11. Experimental Results • Used 200000 tuple sample size • Grew 20 trees on 50000 tuples drawn from pool • Datasets of 1.5 million tuples • Outperforms brute-force method by a factor of 2 or 3

12. Experimental Results • Robust to noise • Noise affects detail-level probability distribution • Affected the lower levels, requiring rescans of small amounts of data • Dynamic updating data • BOAT is much faster than brute-force

13. Weak Points • May not be as useful on complex probability distributions • Failure at high level of tree means that most of the tree is discarded • Hypotheses generate as simple as regular decision trees • Simply a way to speed generation

14. Suggested Improvements • Clustering to give a better sample to draw from • Groups of datapoints with a measure of frequency of occurance • Would give better samples of the data and its underlying probability distribution

15. Suggested Improvements • Extremely large datasets • For TB+ datasets, even two or three passes of DB may be too many • Use MCMC to draw many different samples • Estimate probability density function by resampling • Would not guarantee tree accuracy

16. Conclusion • Effective way to build scalable decision trees • Much faster than the standard method • Useful in large datasets