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Machine learning Introduction
- 1. Machine Learning Introduc1on guodong@hulu.com Machine learning introduc0on Logis1c regression Feature selec1on Boos1ng, tree boos1ng See more ML posts: h>p://dongguo.me/
- 2. Machine Learning Makes Life Be>er
- 3. WHAT IS MACHINE LEARNING?
- 4. Learning • What is learning – Find rules from data/experience • Why learning is possible – Assume rules exist in this world • How to learn – Induc1ve
- 5. What is machine learning • “Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed” -‐ Arthur Samuel (1959) • Machine learning is the study of computer algorithms that improve automa1cally through experience” – Tom Mitchell (1998)
- 6. Overview of machine learning Machine Learning Unsupervised Learning Supervised Learning Classifica1on Semi-‐supervised Learning Regression
- 7. Outline • Supervised Learning • Case Study • Challenge • Resource
- 8. Supervised learning • Concepts • Defini1on • Models • Metrics • Open Ques1ons
- 9. Concepts Problem Generate dataset Dataset Train Sample/instance Feature vector label model Predict Test Model Tuning Feature selec0on
- 10. What is Supervised learning • Find a func1on (from some func1on space) to predict for unseen instances, from the labeled training data – Func1on space: determined by the chosen model – Find the func1on: minimize error on training data with some cost func1on • 2 types: Classifica1on and regression
- 11. Formal defini1on • Given a training dataset r N {xi , yi }i =1 • And define a loss func1on ∧ ∧ L( y, y ), where y = f ( x) • Target ∧ f ( x) =arg min G ( f ), f 1 st. G ( f ) = N N ∑ L( y , f ( x )) i =1 i i
- 12. Models for supervised learning • Classifica1on and regression – For classifica1on: LR(Logis1c regression), Naïve Bayes – For regression: linear regression – For Both: Trees, KNN, SVM, ANN • Genera1ve and Discrimina1ve – Genera1ve: Naïve Bayes, GMM, HMM – Discrimina1ve: KNN, LR, SVM, ANN, Trees • Parametric and nonparametric – Parametric: LR, Naïve Bayes, ANN – nonparametric: Trees, KNN, kernel methods
- 13. Decision Tree • Would you like to date somebody? Gender male female Good looking? Yes! Pass No! umm.. Pass Others… Accept Very good Accept else Pass
- 14. K-‐Nearest Neighbor classifier K=15 K=1
- 15. Naïve Bayes • Bayes classifier • Condi1onal Independence assump1on • With this assump1on
- 16. Logis1c regression • Logis1c func1on
- 19. Model Inference • Typical inference methods – Gradient descent – Expecta1on Maximiza1on – Sampling based
- 20. Model ensemble • Averaging or vo1ng output of mul1ply classifiers • Bagging (bootstrap aggrega1ng) – Train mul1ple base models – Vote mul1ply base classifiers with same weight – Improve model stability and avoid overfihng – Work well on unstable base classifier • Adaboost (adap1ve boos1ng) – Sequen1al base classifiers – Misclassified instances have higher weight in next base classifier – Weighted vo1ng
- 21. Evalua1on metrics • Common Metrics for classifica1on – Accuracy – Precision-‐Recall – AUC • For regression – Mean absolute error (MAE) – Mean square error (MSE), RMSE
- 22. Ques1on1: How to choose a suitable model? Characteris0c Naïve Bayes Trees K Nearest neighbor Logis0c regression Neural SVM Networks Natural handling data of “mixed” type Robustness to outliers in input space Computa1onal scalability Interpretability 1 3 1 1 1 1 3 3 3 3 1 1 3 3 1 3 1 1 2 2 1 2 1 1 Predic1ve power 1 1 3 2 3 3 <Elements of Sta-s-cal Learning> II P351
- 23. Ques1on2: Can we find a 100% accurate model? • Expected risk • Empirical risk • Choose a family for candidate predic1on func1ons • Error
- 24. Case study: Predic1ve Demographic Feature extrac1on (‘show’, ‘ad vote’, ‘ad selec1on’) feature analysis (remove ‘ad selec1on’) Load login profile ML problem? What kind? Labels? Evalua1on metric? Possible features? (show, ad vote, ad selec1on, search…) Accessible? Problem Dataset genera1on Choose a Model 1. Familiar? (NB, ANN, LR, Tree, SVM) 2. Computa1onal cost? Interpretability? Precision? 3. Data: amount? noise ra1o? Train Try more features(add ‘OS’, ‘browser’, ‘flash’) Feature selec1on (remove ‘flash’, and non anonymous features) Predictor Try more models Tuning Evalua1on (AUC, Precision-‐recall) Test Challenges (Noise, different Join distribu1on, evalua1on) model ensemble Predictor on product Scoring Online Update
- 25. Challenges in Machine learning • Data – Sparse data in high dimensions – Limited labels • Computa1on Cost – Speed Up advanced models – Paralleliza1on • Applica1on – Structured predic1on
- 26. Resource • • • • Conference Books Lectures Dataset
- 27. Top conference • • • • • ICML NIPS IJCAI/AAAI KDD Other related – WSDM, WWW, SIGIR, CIKM, ICDE, ICDM
- 28. Books • • • • Machine Learning [link] by Mitchell Pa-ern Recogni0on and Machine Learning [link] by Bishop The Elements of Sta0s0cal Learning [link] Scaling Up Machine Learning [link]
- 29. Lectures • Machine Learning open class – by Andrew Ng – Video in YouTube • Advanced topics in Machine Learning – Cornell • h>p://videolectures.net/
- 30. Other research resource • Research Organs – Yahoo Research [link] – Google Research publica1ons [link] • Dataset – UCI machine learning Repository [link] – kaggle.com
- 31. THANKS
Notas del editor
- Unsupervised learning(聚类，降维（topic model）): learn structure from unlabeled data. Closely related with density estimation; summarize the dataSemi-supervised learning: use both labeled and unlabeled samples for training; It’s cost to collect lots of labels sometimes, use both
- 除此之外，你对模型的熟悉程度。
- Expected risk: 定义好loss function，选择一个预估函数，有一个输入变量和response value的联合分布，在该联合分布上对损失函数求积分，即为期望风险；通过最小化该期望风险，我们找到一个最优的预估函数。但是实际上，我们并不知道该联合分布，我们有的是从该联合分布中有偏或无偏采样得到的有限样本，可能还有一些noise点。我们转为最小化在该有限样本上的最小化loss function寻找预估函数。 即我们转为最小化经验风险另一方面，我们往往给目标函数指定function family，该function family极有可能没有包含最优或者较优的那些目标函数。误差的大小： 第一部分：函数family F中的预估函数有多接近真正最优的预估函数；第二部分：我们选择优化经验而不是经验风险
- Logistic regression is one of the most popular classifier.Advantage: 1. easy understand and implement; 2. not bad performance; 3. light weight and less time taken for training and prediction;(can handle large dataset) 4. easy parallelizationValue to attendances:Know about what is logistic regression, what’s the advantages and disadvantage. what kind of problems are suitable apply to.L1 and L2 regularizationHow to inference through maximize likelihood with gradient descent. And know how to implement it
- 对于generalized linear model，如果response variable是binomial或者multinomial分布，且选择了logit function作为link function 就是logistic regressionLogistic function 是logit function的反函数
- Binary(binomial) logistic regression
- 负梯度方向是使得函数值下降最快的方向
- 在重新计算likelihood前，我们看一下这2种正则化背后的理论基础
- 假设全部的weight服从一致的分布
- Laplace 分布一阶倒数不连续假设全部的weight服从一致的分布（均值为0，Laplace参数也一样）W_k在一次更新中不能变换正负号
- L1拟合得到的weight通常较稀疏，带来2点好处： 帮我们做特征选择，工程上更有利
- 增加了decay ratio：AUC稍有提高（0.845 -> 0.847）在不同step时，适合的decay ratio也不一样Iteration times： 与样本量的大小有关
- 例子：今天是高考第一天，高考选专业，每个人有多个候选，但是仅能选择一门专业（计算机，金融，化学，数学，物理，生物）和binomial分布应用的区别多类问题，可以转化为多个两类问题，如果我们的问题是“找出每门课成绩前10%的学生”，我们可以用两类logistic regression来做如果问题是“对于每个学生找出其成绩最好的课，或者最好的几门课”，两类问题就不是很适合 （每一类上的预估概率之和不等于1，无法比较不同类上的概率）Multi-nominal适用于response value为category的情况，不太适合ordinal的情况。我实现了。
- Link function: (1) generalized linear model的重要组成部分：将linear regression拓展到generalized linear model；（2）link function的反函数的自变量介于(-无穷，+无穷)，若y服从binominal分布，应变量介于【0,1】区间The inverse of any continuous cumulative distribution function (CDF) can be used for the link since the CDF’s range is [0,1]
- Generalized linear model 广义上的线性模型，都有一个基本的线性单元W*X（linear regression），通过各种link function建立该线性单元和各种分布的response variable的关系。包含linear regression （normal distribution），logistic regression （binominal/multi-nominal distribution）, Poisson regression (Poisson distribution)对于binominal/multi-nominal distribution，我们也可以选择除logit link function之外的link function （广义的logistic regression）