Features?

• Features are inputs that help us predict some outcome
  • Features = Predictors
  • Outcome = Response / Label
• Feature types
  • Continuous
    • money
    • time
    • height
    • distance
  • Categorical (Discrete)
    • gender
    • zipcode
    • state
    • color
    • Social Security Number

Continuous Features

• Easy to use
• Nothing really needs to be done
  • Transformations
  • Just improving it

Categorical Features

• Harder to use
• Two different types
  • Ordinal
    • Only belong to one category at a time
    • There's some kind of order to the categories
      • Low
      • Medium
      • High
    • Can be fudged into a continuous variable
  • Nominal
    • Only belong to one category at a time

Categorical to usable

• RED, GREEN, BLUE
• One-hot encoding
  • Most common - for model building
    • RED: [1, 0, 0]
    • GREEN: [0, 1, 0]
    • BLUE: [0, 0, 1]
    • PURPLE: [1, 0, 1]
• Dummy encoding
  • Have a control group - testing for differences among groups
  • RED: [0, 0]
  • GREEN: [1, 0]
  • BLUE: [0, 1]

Categorical to usable

• Effects Encoding
  • Comparing one group to all groups
  • Comparison made at the mean of all groups combined
  • Group of least interest coded with -1

  Nationality	C1	C2	C3
  French	0	0	1
  Italian	1	0	0
  German	0	1	0
  Other	−1	−1	−1

Categorical to usable

• Contrast Coding
  • The sum of the contrast coef. equal zero.
  • The difference between the sum of the + coef. and the sum of the - coef. equals 1.
  • Coded variables should be orthogonal

    Nationality	C1	C2
    French	+0.25	+0.50
    Italian	+0.25	−0.50
    German	−0.50	0

  • Hypothesis 1: French and Italian persons will score higher on optimism than Germans (French = +0.25, Italian = +0.25, German = −0.50).
  • Hypothesis 2: French and Italians are expected to differ on their optimism scores (French = +0.50, Italian = −0.50, German = 0).
• Nonsense Coding

Categorical - Binary encoding

Categorical - Ranking

• Figure out some way to rank them that makes sense

• Pattern? 🌈

Categorical - Embedding

• Useful in Neural Networks
• Encode categoricals into a set of continuous numbers
  • You get to pick the number of continuous numbers
  • Rule of thumb $\sqrt[4]{n}$ (where $n$ number of distinct categories)
• Neural Networks don't work well with one-hot encodings
• Word Embedding

Categorical - Vector

• Something new
  • Take all categorical fields and generate a vector
  • Sort of like word2vec
  • Hopefully I'll find a good coding of it

Coming up with Features

• Feature engineering is more important than modeling!
• A better model can raise accuracy by centimeters
• Better predictors can raise accuracy by meters
• Feature Engineering
  • Model only as good as what you put into it
    • New ways to look at the data
    • New data
  • One of my favorite parts of the job
    • Let your imagination run free
    • When I interview I dig heavily into this
  • Unsupervised learning
  • Story-time
    • "6k+ Feature Bank"
    • "Data Bank"

Horse Racing

• Running Style
  • Early (E)
  • Early Presser (EP)
  • Presser (P)
  • Sustained? (S)
• Look at historical races 🎰:
  • Starting Pos, 1st / 2nd / final Call Position
  • 1st / 2nd / Final Call Beaten Length
  • Assign the horses one of the running styles
• Labeled 40 races this way
• A decision tree modeled it perfectly!

Baseball

• Elo rating system
  Used in chess to rank players
• Baseball statistics
  • Baseball nerds everywhere 🤓
  • Use it to your advantage

Automated Valuation Models (AVM)

• Predicting apartment complex values in Florida
• Address to lat/lon
• Shapefiles with coasts
  • Line segments of the coastline
• Calculated shortest distance to coastline
  • AVM goes up closer to coast
  • Dependant on area

Document Scanning Predictions

• Predicting "Date Due" from an invoice
• Optical Character Recognition (OCR)
  • Know where every character/word is on the page
• Find correct "Date Due" on the document
• Generate a Heatmap of correct "Date Due" pixels
  • Scale all points so 1 is the highest number
  
• Predictor: For any candidate date
  • average the value of the "pixels" it covers from the heat map
  • Higher the number the better

Too many Categories

• Fraud Model
• Unsupervised on all the predictors
  • Number of clusters in 1000s
    • Crazy!
  • Crazy Brilliant
    • Fraud is a rare event
    • Most clusters were garbage
    • Others full of fraud
    • Inspected them
  • Grouped these bad clusters together
  • Extra boolean feature fed into the final fraud model

Bonus - Proximity to an internation airport

Failures > Success

• What features are good?
• Plot it!
• Rank features from best to worst
  • p-value / Z-score (with caveats)
  • Binning and difference with Mean (weighted / unweighted)
  • Random Forest Variable Importance Ranking

Plotting

• Crazy Important 🧼📦:
• See the actual relationship
• Predictor / Response type Dependant
  • Response: Boolean / Categorical
    • Predictor: Boolean / Categorical
      • Heatplot
    • Predictor: Continous
      • Violin plot on predictor grouped by response
      • Distribution plot on predictor grouped by response
  • Response: Continuous
    • Predictor: Boolean / Categorical
      • Violin plot on response grouped by predictor
      • Distribution plot on response grouped by predictor
    • Predictor: Continuous
      • Scatter plot with trendline

Categorical Response / Categorical Predictor

Categorical Response / Categorical Predictor

Categorical Response / Continuous Predictor

Categorical Response / Continuous Predictor

Continuous Response / Categorical Predictor

Continuous Response / Categorical Predictor

Continuous Response / Continuous Predictor

An extra reason to plot

• Target Leakage
  • Nostradamus Variables
  • Accidentally leak the response into the predictor
• Obvious when plotted
  • If model is too good
  • Always check the best performing predictors
• Need to rank predictors

Random Forest

• Variable Importance
  • Great for variable ranking
  • sklearn has this as feature_importances_
• Good segwe into decision trees

Decision Trees

• How do they work?
  • Look at all the data for a branch
    • Look at all the predictors, how do we maximize
    • Gini
    • Information Gain (Entropy)
    • Split on it
  • Repeat until we have everything categorized correctly or hit stopping criteria
    • Overfits!
      • Fit to the noise
      • Don't over grow
    • Pruning
      • Cross Validation
      • Where to cut branches
• sklearn.tree.DecisionTreeClassifier

Decision Tree - Building

Fisher's Iris Data set

• 150 observations
  • 50 samples from each of the species of Iris
    • Iris setosa
    • Iris virginica
    • Iris versicolor
• 4 predictors
  • Sepal length
  • Sepal width
  • Petal length
  • Petal width

Decision Tree - Building

Decision Tree - Original Dataset

              ********************************************************************************
              Original Dataset
              ********************************************************************************
                  sepal_length  sepal_width  petal_length  petal_width           class
              0             5.1          3.5           1.4          0.2     Iris-setosa
              1             4.9          3.0           1.4          0.2     Iris-setosa
              2             4.7          3.2           1.3          0.2     Iris-setosa
              3             4.6          3.1           1.5          0.2     Iris-setosa
              4             5.0          3.6           1.4          0.2     Iris-setosa
              ..            ...          ...           ...          ...             ...
              145           6.7          3.0           5.2          2.3  Iris-virginica
              146           6.3          2.5           5.0          1.9  Iris-virginica
              147           6.5          3.0           5.2          2.0  Iris-virginica
              148           6.2          3.4           5.4          2.3  Iris-virginica
              149           5.9          3.0           5.1          1.8  Iris-virginica

              [150 rows x 5 columns]
              

Decision Tree - Building

Decision Tree - Unpruned Tree

Decision Tree - Pruning

Decision Tree - Cross Validation Score

Decision Tree - Plotting the score

Decision Tree - Pruning Plot

Decision Tree - Plotting the optimal tree

Decision Tree - Pruned Tree

Decision Tree - Overfitting

• Probably the biggest problem
• Running this on the titanic dataset

Full Tree

as PDF

Prunned Tree

as PDF

Titanic - Cross Val Negative Mean Squared Error

Bootstrap Aggregating (Bagging)

• Bootstrap
  • Sample with replacement from dataset
• Aggregating
  • Build many trees and agregate their results
• sklearn.ensemble.BaggingClassifier
  • Wrapper for any classifier

Bagging - Code

Bagging - Trees

PDF

PDF

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Random Forest

• Original Paper
• Builds on bagging
  • Many trees (aggregate)
    • Each tree built from a sample of values (bootstrap)
    • Each split picks from a random subset of predictors
      • Usually $ \sqrt{n} $
      • $n = $ number of predictors

Random Forest - Variable Importance

• Impurity Based feature importance (Gini importance)
  • sklean.ensemble.RandomForestClassifier.feature_importances_
  • Uses the out-of-bag error 🤷
  • Biased toward categorical predictors with high cardinality
• Permutation based feature importance
  • sklearn.inspection.permutation_importance
  • Will actually work with any classifier!

In Summary

• Features
  • Continuous: Easy
  • Categorical: Harder
    • One-hot
    • Dummy
    • Ranking
    • Embedding
    • Vector
• Feature Engineering
  • Best way to increase a model's accuracy
  • Always plot predictons!
  • Variable Importance rankings
    • p-value & z/t score
    • Difference with mean of response
    • Random Forest variable importance