Uploaded byFrancesco Mosconi
Machine Learning: Techniques, Best Practices and Practical Application
The document outlines topics related to machine learning, including techniques, applications, and model evaluation methods. It emphasizes the importance of choosing the right ML technique, data processing, and model building, along with practical applications like sentiment analysis and prediction models. Additionally, it covers various machine learning models such as logistic regression, neural networks, and decision trees, as well as evaluation metrics like accuracy, precision, and recall.
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Machine Learning: Techniques, Best Practices and Practical Application
- 1. Catalit LLC MACHINE LEARNING FrancescoMosconi, PhD Data Weekends & CATALIT LLC Data Weekends Catalit Techniques, Best Practices and Practical Application
- 2. Catalit LLC INSTALL • whileI talk ….. download and install • Anaconda Python 3.6 • get code at www.dataweekends.com/tdwi
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- 5. Catalit LLC THIS SESSION •Recognize problems & choose right ML technique • Load data with Pandas • Build classification model with Scikit-Learn • Evaluate model performance with Scikit-Learn
- 6. Catalit LLC YOUR BRAIN… …AGREAT PATTERN RECOGNIZER http://scriptoriumdaily.com/
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- 14. Catalit LLC APPLICATIONSSentiment AnalysisBookrecommendation Human recognition House price prediction Document classification
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- 21. Catalit LLC PREDICTIONTYPE CATEGORICAL CONTINUOUS Eyecolors Height of children Courses at university Weight of cars Highest degree Speed of the train Gender Temperature Spam or not Stock price
- 22. Catalit LLC COMBINED CATEGORICAL CONTINUOUS SUPERVISEDCLASSIFICATION REGRESSION UNSUPERVISED CLUSTERING
- 23. Catalit LLC APPLICATIONSSentiment AnalysisBookrecommendation Human recognition House price prediction Document classification
- 24. Catalit LLC BINARY CLASSIFICATION Charges Has Internet Internet Type TenureChurn Client 1 62.43 No No Internet 23 No Client 2 180.23 Yes DSL 6 No Client 3 670.85 Yes Fiber 18 Yes Features Labels Data Point
- 25. Catalit LLC ML STEPS 1. Collection 2. Processing 3.Model Building 4. Evaluation 5. Deployment
- 26. Catalit LLC BENCHMARK • What’sthe dumbest model you can think of?
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- 29. Catalit LLC LOGISTIC REGRESSION probabilityof belonging to class value of independent variable • Advantages: • simple function • can be parallelized • large scale GOAL: minimize squared error for probability
- 30. Catalit LLC NEURAL NETWORKS Inputlayer Several hidden layers Output layer • Advantages: • Unstructured data • No limit to complexity • Great on large datasets
- 31. Catalit LLC DECISIONTREES Comedy? Yes No WatchForeign Film Yes No Watch Pass • Advantages: • easy to interpret • fast prediction • rules based
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- 36. Catalit LLC CONFUSION MATRIX •Accuracy: Overall, how often is it correct? • (TP +TN) / total Test Negative Test Positive Condition Negative TRUE NEGATIVE FALSE POSITIVE (Type I error) Condition Positive FALSE NEGATIVE (Type II error) TRUE POSITIVE
- 37. Catalit LLC CLASSIFICATION SCORES •Accuracy: Overall, how often correct? • (TP +TN) / total • Precision: Test positive, how often prediction correct? • TP / test yes • Recall: Actual value positive, how often prediction correct? • TP / actual yes Test Negative Test Positive Condition Negative TRUE NEGATIVE FALSE POSITIVE (Type I error) Condition Positive FALSE NEGATIVE (Type II error) TRUE POSITIVE
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- 39. Catalit LLC CROSSVALIDATION Round 1 Test Train Train Train Train 93% Round2 Train Test Train Train Train 89% Round 3 Train Train Test Train Train 91% Round 4 Train Train Train Test Train 92% Round 5 Train Train Train Train Test 88% Accuracy = Average(Rounds)
- 40. Catalit LLC LEARNING CURVE Moredata or better model? 0.7 0.8 0.9 1 1.1 0 300 600 900 1200 1500 Test Scores Train Scores
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- 44. Catalit LLC ANY QUESTIONS? remember to give feedback using the app www.catalit.com info@catalit.com