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Introduction to XGboost
- 1. School of Computer Sicience and
- 2. 1. Introduction 2. Boosted Tree 3. Tree Ensemble 4. Additive Training 5. Split Algorithm School of Computer Sicience and
- 3. 1 Introduction • What Xgboost can do ? School of Computer Sicience and Binary Classification Multiclass Classification Regression Learning to Rank By 02. March.2017 Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library Support Language • Python • R • Java • Scala • C++ and more Support Platform • Runs on single machine, • Hadoop • Spark • Flink • DataFlow
- 4. 2 Boosted Tree • Variants: • GBDT: gradient boosted decision tree • GBRT: gradient boosted regression tree • MART: Multiple Additive Regression Trees • LambdaMART, for ranking task • ... School of Computer Sicience and
- 5. 2.1 CART • CART: Classification and Regression Tree • Classification • Three Classes • Two Variables School of Computer Sicience and
- 6. 2.1 CART Prediction • predicting price of 1993-model cars. • standardized (zero mean,unit variance) School of Computer Sicience andpartition
- 7. 2.1 CART • Information Gain • Gain Ratio • Gini Index • Pruning: prevent overfitting School of Computer Sicience and Which variable to use for division
- 8. 2.2 CART • Input: Age, gender, occupation • Goal: Does the person like computer games School of Computer Sicience and
- 9. 3 Tree Ensemble • What is Tree Ensemble ? • Single Tree is not powerful enough • Benifts of Tree Ensemble ? • Very widely used • Invariant to scaling of inputs • Learn higher order interaction between features • Scalable School of Computer Sicience and Boosted Tree Random Forest Tree Ensemble
- 10. 3 Tree Ensemble School of Computer Sicience and Prediction of is sum of scores predicted by each of the tree
- 11. 3 Tree Ensemble-Elements of Supervised Learning • Linear model School of Computer Sicience and Optimizing training loss encourages predictive models Opyimizing regularization encourages simple models
- 12. 3 Tree Ensemble • Assuming we have k trees School of Computer Sicience and • Parameters • Including structure of each tree, and the score in the leaf • Or simply use function as parameters • Instead learning weights in R^d, we are learning functions ( trees)
- 13. 3 Tree Ensemble • How can we learn functions? School of Computer Sicience and The height in each segment Splitting positions • Training loss: How will the function fit on the points? • Regularization: How do we define complexity of the function?
- 14. 3 Tree Ensemble School of Computer Sicience and Regularization Number of splitting points L2 norm of the leaf weights Training loss: error =
- 15. 3 Tree Ensemble • We define tree by a vector of scores in leafs, and a leaf index mapping function that maps an instance to a leaf School of Computer Sicience and
- 16. 3 Tree Ensemble • Objective: • Definiation of Complexity School of Computer Sicience and
- 17. 4 Addictive Training (Boosting) • We can not use methods such as SGD, to find f ( since thet are trees, instead of just numerical vectors) • Start from constant prediction, add a new function each time. School of Computer Sicience and
- 18. 4 Addictive Training (Boosting) • How do we decide which f to add ? • The prediction at round t is • Consider square loss School of Computer Sicience and
- 19. 4 Addictive Training (Boosting) • Taylor expansion of the objective • Objective after expansion School of Computer Sicience and
- 20. 4 Addictive Training (Boosting) • Our new goal, with constants removed • Benifits School of Computer Sicience and
- 21. 4 Addictive Training (Boosting) • Define the instance set in leaf j as • Regroup the objective by each leaf • This is sum of T independent quadratic functions • Two facts about single variable quadratic function School of Computer Sicience and
- 22. 4 Addictive Training (Boosting) • Let us define • Results School of Computer Sicience and There can be infinite possible tree structures
- 23. 4 Addictive Training (Boosting) • Greedy Learning , we grow the tree greedily School of Computer Sicience and
- 24. 5 Spliting algorithm • Efficeint finding of the best split • What is the gain of a split rule xj < a ? say xj is age School of Computer Sicience and All we need is sume of g and h in each side, and calculate • Left to right linear scan over sorted instance is enough to decide the best split
- 25. 5 Spliting algorithm School of Computer Sicience and
- 26. 5 Spliting algorithm School of Computer Sicience and
- 27. 5 Spliting algorithm School of Computer Sicience and
- 28. References • http://www.52cs.org/?p=429 • http://www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf • http://www.sigkdd.org/node/362 • http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf • http://www.stat.wisc.edu/~loh/treeprogs/guide/wires11.pdf • https://github.com/dmlc/xgboost/blob/master/demo/README.md • http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/ • http://xgboost.readthedocs.io/en/latest/model.html • http://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine- learning/ School of Computer Sicience and
- 29. Suplementary • Tree model, works very well on tabular data, easy to use, and interpret and control • It can not extrapolate • Deep Forest: Towards An Alternative to Deep Neural Networks, Zhi-Hua Zhou, Ji Feng, Nanjing University • Submitted on 28 Feb 2017 • Comparable performance and easy to train (less parameters) School of Computer Sicience and
- 30. School of Computer Sicience and
Notas do Editor
- XGBoost is one of the most frequently used package to win machine learning challenges XGBoost can solve billion scale problems with few resources and is widely adopted in industry. XGBoost is an optimized distributed gradient boosting system designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples. The most recent version integrates naturally with DataFlow frameworks(e.g. Flink and Spark)
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- Fitting well in training data at least get you close to training data which is hopefully close to the underlying distribution Simpler models tends to have smaller variance in future predictions, making prediction stable
- 1. Almost half of data mining competition are won by using some variants of tree ensemble methods 2. so you do not need to do careful features normalization 3. and are used in Industry