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Features Engineering Art using R

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Features Engineering Art using R

  1. 1. FEATURE ENGINEERING ART USING R MOHAMMED ALI MACHINE LEARNING ENG.
  2. 2. AGENDA What we will cover Why we need Feature Engineering What is Feature Engineering Art? Machine Learning Process Feature Engineering Process Manual Construction Automatic Construction Feature Selection Workshop
  3. 3. EXPECTATIONS What we will NOT cover The Iceberg Illusion What we will cover What we will NOT cover
  4. 4. OVERVIEW Why we need Feature Engineering?
  5. 5. OVERVIEW What is Feature Engineering Art? Feature Engineering Art
  6. 6. OVERVIEW What is Feature Engineering Art? Feature Feature is an individual measurable property or characteristic of a phenomenon being observed. Raw Data Feature Model Insight Feature Engineering Art
  7. 7. OVERVIEW What is Feature Engineering Art? Feature engineering is manually designing what the input x’s should be. (Tomasz Malisiewicz) You have to turn your inputs into things the algorithm can understand. (Shayne Miel) Feature Engineering Art
  8. 8. OVERVIEW What is Feature Engineering Art? Transformation Feature Engineering Art
  9. 9. OVERVIEW What is Feature Engineering Art? Transformation Feature Engineering Art
  10. 10. OVERVIEW What is Feature Engineering Art? Transformation Merging + = Feature Engineering Art
  11. 11. OVERVIEW What is Feature Engineering Art? Transformation Merging + = Feature Engineering Art
  12. 12. OVERVIEW What is Feature Engineering Art? MergingTransformation Splitting Feature Engineering Art Day Active Night Active
  13. 13. OVERVIEW What is Feature Engineering Art? Merging Crafting Transformation Splitting Feature Engineering Art
  14. 14. OVERVIEW What is Feature Engineering Art? Merging Crafting Transformation Splitting You have to turn your inputs into things the algorithm can understand. Shayne Miel Feature Engineering Art
  15. 15. OVERVIEW What is Feature Engineering Art? Feature Engineering Art Is Feature Engineering an ART ?
  16. 16. OVERVIEW What is Feature Engineering Art? Feature Engineering Art YES, you need to create the right question to get the right answer
  17. 17. OVERVIEW What is Feature Engineering Art? Feature Engineering Art
  18. 18. MACHINE LEARNING PROCESS Data Collection Data Cleaning Feature Engineering Model Training Data Testing Results Improvement
  19. 19. FEATURE ENGINEERING PROCESS Evaluate Model Select Features Brainstorm Features Devise Features
  20. 20. HOW TO ENGINEER Manual Construction + + + Base Data Frame Wednesday, March 12,2018, 12:20:59 am Features built one at a time by hand from given data set (S) using domain knowledge 123L - 12.5kg - 15$Simple Text
  21. 21. HOW TO ENGINEER Deep Feature Synthesis Features are derived from relationships between the data points in a dataset. Across datasets, many features are derived by using similar mathematical operations. New features are often composed from utilizing previously derived features. Problem specific. Error-prone. Limited both by human creativity and patience.
  22. 22. HOW TO ENGINEER Automatic Construction + + + Generated Feature DataSetandTransformationFunction
  23. 23. DISCUSSION What to use and when ………
  24. 24. FEATURE SELECTION Why Feature Selection ? Models Simplification. Shorter Training Times. Improve Model Accuracy. Enhanced Generalization by Reducing Overfitting.
  25. 25. FEATURE SELECTION Feature Selection Methods Embedded MethodsFilter Methods Wrapper Methods Feature Selection Methods Filter Methods considers the relationship between features and the target variable to compute the importance of features. In wrapper methods, we try to use a subset of features and train a model using them. Based on the inferences that we draw from the previous model, we decide to add or remove features from your subset.Forward Selection Backward Selection Recursive Feature Elimination Feature selection can also be acheived by the insights provided by some Machine Learning models.
  26. 26. WORKSHOP Titanic A Disaster to Learn From …
  27. 27. WORKSHOP Titanic
  28. 28. WORKSHOP Titanic Feature 1 What is in Name? Title Feature 2 Family swim together ? Maybe Feature 3 Can I buy new life ? Most probably NO
  29. 29. WORKSHOP Startups Chase the Vision not the Money …
  30. 30. QUESTIONS

Hinweis der Redaktion

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • transformation:
    a: invoice date  season, Occasions. Weekend
    b. Age  categorical (young, teenage, adult, senior)

    2. Merging
    A: weight, height  BMI
    b. Startup date, yearly profilt or loose - growth rate

    3. Splitting
    a.Call records  day active or night active

    4. Crafting feature
    Scrapping social media for customer  offers (card ahlawy)

  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs
  • Which technique to be applied and when
    You should have vision to extract features
    Know the right question to get the right answer

    3awz azwd arba7,
    ytl3 mno el seasons w el holidays
    A3ml sales ll setat aw fe2a mo3yna
    A customize bona2n 3la customer needs

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