Beginner's Guide to Machine Learning Competitions, Europython 2015

This tutorial will offer a hands-on introduction to machine learning and the process of applying these concepts in a Kaggle competition. We will introduce attendees to machine learning concepts, examples and flows, while building up their skills to solve an actual problem. At the end of the tutorial attendees will be familiar with a real data science flow: feature preparation, modeling, optimization and validation.

Transcript

1. Beginner’s guide to Machine Learning competitions Christine Doig EuroPython 2015
2. Christine Doig Data Scientist, Continuum Analytics ch_doig chdoig chdoig.github.io
3. Data Science Machine Learning Supervised learning Classification Kaggle Competitions Dataset Setup Feature preparation Modeling Optimization Validation Anaconda Concepts Process NLP Sentiment analysis
4. Data Science Machine Learning Supervised learning Classification Kaggle Competitions Dataset Setup Feature preparation Modeling Optimization Validation Anaconda Concepts Process NLP Sentiment analysis 45min 10 min 1h 5 min 1h
5. Data Science Machine Learning Supervised learning Classification Concepts NLP Sentiment analysis
6. Data Science
7. Slides: http://www.slideshare.net/joshwills/production-machine-learninginfrastructure Video: https://www.youtube.com/watch?v=v-91JycaKjc From the lab to the factory - Data Day Texas data science http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf http://www.experfy.com/blog/become-data-scientist/ http://www.fico.com/landing/infographic/anatomy-of-a-data-scientist_en.html
8. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web
9. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer
10. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer Model Algorithm Report Application Pipeline/ Architecture
11. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Models Deep Learning Supervised Clustering SVM Regression Classification Crossvalidation Dimensionality reduction KNN Unsupervised NN Filter Join Select TopK Sort Groupby min summary statistics avg max databases GPUs arrays algorithms performance compute SQL Reporting clusters hadoop hdfs optimization HPC graphs FTT HTML/CSS/JS algebra stream processing deployment servers frontend sematic web batch jobs consistency A/B testing crawling frameworks parallelism availability tolerance DFT spark scraping databases apps NOSQL parallelism interactive data viz pipeline cloud Developer
12. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer PyMC Numba xlwings Bokeh Kafka RDFLib mrjob mrjob
13. Machine Learning
14. Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling
15. Exploratory Predictive Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling
16. Exploratory Predictive Machine Learning Unsupervised learning Supervised learning Classification Regression labels no labels categorical quantitative id gender age job_id 1 F 67 1 2 M 32 2 3 M 45 1 4 F 18 2 group similar individuals together id gender age job_id buy/click_ad money_spent 1 F 67 1 Yes $1,000 2 M 32 2 No - 3 M 45 1 No - 4 F 18 2 Yes $300 predict whether an individual is going to buy/click or not Classification Regression predict how much is the individual going to spend
17. Natural Language Processing
18. Machine Learning Natural language processing field concerned with the interactions between computers and human (natural) languages Sentiment analysis Extract subjective information on polarity (positive or negative) of a document (text, tweet, voice message…) ! e.g online reviews to determine how people feel about a particular object or topic. tasks
19. Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling Sentiment analysis Movie review Positive Negative e.g.
20. Setup
21. Kaggle Competitions Dataset Setup Anaconda
22. Setup options You already have Python installed and your own workflow to install Python packages happy alternative Install dependencies in README Anaconda Miniconda + conda env Free Python distribution with a bunch of packages for data science too many packages!!! Python + conda (package manager) git clone git@github.com:chdoig/ep2015-ml-tutorial.git cd ep2015-ml-tutorial conda env create source activate ep-ml http://conda.pydata.org/miniconda.html http://continuum.io/downloads Python + conda (package manager) + packages
23. Kaggle https://www.kaggle.com/ hosts online machine learning competitions
24. Kaggle Competition https://www.kaggle.com/c/word2vec-nlp-tutorial
25. Kaggle Competition https://www.kaggle.com/c/word2vec-nlp-tutorial Bag of Words Meets Bags of Popcorn Data T ask 50,000 IMDB movie reviews predict the sentiment for each review in the test data set 25,000 rows containing an id, sentiment, and text for each review. labeledTrainData.tsv testData.tsv 25,000 rows containing an id and text for each review
26. Feature preparation Modeling Optimization Validation Process
27. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble
28. Feature preparation Feature extraction the process of making features from available data to be used by the classification algorithms Reviews M N Words Model Evaluation Metrics Visualizations NaiveBayes DecisionT rees Feature extraction id sentiment review count_words terrible_word 1 0 the movie was terrible 4 1 2 1 I love it 3 0 3 1 Awesome! Love it! 3 0 4 0 I hated every minute 4 0
29. T okenization Stopwords transition, metal, oxides, considered, generation, materials, field, electronics, advanced, catalysts, tantalum, v, oxide, reports, synthesis, material, nanometer, size, unusual, properties… transition_metal_oxides, considered, generation, materials, field, electronics, advanced, catalysts, tantalum, oxide, reports, synthesis, material, nanometer_size, unusual, properties, sol_gel_method, biomedical_applications… transition, metal_oxides, tantalum, oxide, nanometer_size, unusual_properties, dna, easy_method, biomedical_applications transition, metal_oxides, generation, tantalum, oxide, nanometer_size, unusual_properties, sol, dna, easy_method, biomedical_applications Simple Collocations Entities Combination Lemmatization transition, metal, oxide, consider, generation, material, field, electronic, advance, catalyst, property… language generic domain specific a! above! across! after! afterwards! again! against! all ! … material! temperature! advance! size! …. Feature extraction Text
30. Vector Space Corpus - Bag of words Dictionary 1 - transition! 2- metal! 3- oxides! 4- considered! … ! [(0, 1), (1, 1), (2, 1)] [(0, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)] [(2, 1), (5, 1), (7, 1), (8, 1)] [(1, 1), (5, 2), (8, 1)] [(3, 1), (6, 1), (7, 1)] [(9, 1)] [(9, 1), (10, 1)] [(9, 1), (10, 1), (11, 1)] [(4, 1), (10, 1), (11, 1)]
31. Feature_extraction.ipynb
32. Modeling Naive Bayes Classifier P(A|B) = P(B|A) * P(A) / P(B) id sentiment review count_words terrible_word 1 0 the movie was terrible 4 1 2 1 I love it 3 0 3 1 Awesome! Love it 1 0 4 0 I hated every minute 4 0 P(1 | love) = P(love | 1) * P(1) / P(love) = (2/2 * 2/4)/(2/4) = 100% What’s the probability of the review being positive if the word love appears in the review?
33. Modeling.ipynb
34. occurs whenever a model learns from patterns that are present in the training data but do not reflect the data-generating process. Seeing more than is actually there. A kind of data hallucination. Validation Overfitting http://talyarkoni.org/downloads/ML_Meetup_Yarkoni_Overfitting.pdf
35. T raining data Validation Model Evaluate New data Evaluate
36. Validation.ipynb
37. Validation Hold out method T raining data T est data accuracy
38. Crossvalidation Test Training ! + ! Validation Training Validation Accuracy = average(Round1, Round 2….) Final Accuracy one shot at this! Accuracy in each round with validation set
39. Confusion matrix Validation Positive reviews Negative reviews 95% 5% Accuracy 95% Real Model prediction
40. model/real positive negative positive 95 5 negative 0 0 Confusion matrix Validation
41. ROC curve/ AUC Validation true positive false positive 100% true positive 0 % false positive
42. ROC curve/ AUC Validation true positive false positive 100% true positive 0 % false positive AUC
43. Kaggle leaderboard
44. Optimization Ensemble methods Classifier 1 Classifier 2 Classifier 3 id cls_1 cls_2 cls_3 ensemble 1 0 0 0 0 2 0 1 1 1 3 1 1 1 1 4 0 0 1 0 e.g. majority voting w1 w2 w3 e.g. weighted voting
45. Kaggle forums
46. Data Science Machine Learning Supervised learning Classification Kaggle Competitions Dataset Setup Feature preparation Modeling Optimization Validation Anaconda Concepts Process NLP Sentiment analysis
47. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble