Visualization of Enhanced Spark Induced Naive Bayes Classifier with Barry Becker

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This document discusses visualizing a naive Bayes classifier in Spark. It begins with an overview of naive Bayes classifiers and their advantages of being fast and simple to visualize but assuming independent features. It then discusses using a mushroom dataset to classify mushrooms as edible or poisonous. The document explains Bayes' rule and demonstrates it. It notes limitations of Spark's naive Bayes and how to address different data types like strings, nulls, and continuous values using techniques like MDLP discretization. It concludes with a demo of these techniques and discusses the benefits of visualization for model insight.

Dados e análise

Barry Becker, ESI-Group
VISUALIZATION OF
ENHANCED SPARK
INDUCED NAIVE BAYES
CLASSIFIER

Overview
• What the Naïve Bayes Classifier is
• How it can be visualized, and why you would want to
• Limitations of the version in spark, how to get around them
• Demo and use cases

What is a Naïve Bayes Classifier?
• A probabilistic model that can be used to predict a
categorical value (the target)
• Induced using training data and evaluated with test data
Inducer
Classifier
Training Data
New Data Predictions

What is a Naïve Bayes Classifier?
• Advantages
– Fast and simple
– Easy to visualize
• Disadvantage
– Assumes features are independent

Use Case: Detecting Poisonous Mushrooms
Expert determines edibility based on
cap shape, odor, gill spacing, stalk root,
veil type, veil color, ring type, spore print color,
habitat, gill size, stalk shape, … many more….
for 8,124 cases
https://www.pinterest.com/pin/239253798926772284

Bayes’ Rule
P(Edible | X) = P(X | Edible) P(Edible) / P(X)
Where X is
Odor = none and
Spore print color = chocolate and
Ring type = evanescent
Bayes Rule Demo

Understand
model with
Visualization
Everything you see is
based on counts

How the
Model
Makes
Predictions
Multiplies conditional
probabilities to come
up with a posterior
probability

Limitations of Spark Naïve Bayes
Out-of-the-box version is only for document classification!
Document Classification Approach
• Each feature represents a word
• For each feature,
determine frequency for each class
More General Approach
• Each feature is a column
• For each value of each feature,
determine class distribution
=* …**
Prediction:
Not Spam
loan viagra zebra
=**
Prediction:
Poisonousevanescent none chocolate
Ring Type: Odor: SP Color:
spam
not spam
edible
poisonous

Limitations of Spark Naïve Bayes
All columns must be non-negative integers
– But what if there are nulls?
– But what if there are strings?
– But what if there are floating point values?
– But what if there are dates?
– What if target is continuous?

How to Handle String Columns?
• First replace Nulls with a special Null value
• Use StringIndexers to map string values to
integer indices
• Lastly, use IndexToString transformer to restore
the predicted value back to a string.

How to Handle Continuous Columns?
• Use MDLP Discretization to intelligently bin
continuous (number or date) columns with
respect to the target
– Entropy based binning makes distributions of
adjacent bins as different as possible
• Replace Nulls with NaN so they can be in a
separate bin.

MDLP applied to a continuous column
MDLP Binning
30 Uniform Bins
HistogramView

MDLP applied to continuous column
MDLP Binning
30 Uniform Bins
SpinogramView

MDLP Binning of Continuous Features
Splits seek to maximize the information, as measured by
entropy
Recursively split bins until
– Information gain is too small
– Bins are getting too small (avoids overfitting)
Open Source
https://github.com/sramirez/spark-MDLP-discretization

What if continuous Target?
Automatically bin
a continuous target
into 3 equal weight bins

Pixel-Perfect Rendering
Anti-aliased rendering
Pixel-perfect rendering

Conclusion
• Spark Naïve Bayes classifier can be applied to
any data as long as some modifications made
• MDLP is useful for binning continuous features
• Visualization of model provides insight, trust,
and ability to answer what If questions

https://cloud.esi-group.com/analytics
Barry Becker - bbe@esi-group.com

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1. Barry Becker, ESI-Group VISUALIZATION OF ENHANCED SPARK INDUCED NAIVE BAYES CLASSIFIER

2. Overview • What the Naïve Bayes Classifier is • How it can be visualized, and why you would want to • Limitations of the version in spark, how to get around them • Demo and use cases

3. What is a Naïve Bayes Classifier? • A probabilistic model that can be used to predict a categorical value (the target) • Induced using training data and evaluated with test data Inducer Classifier Training Data New Data Predictions

4. What is a Naïve Bayes Classifier? • Advantages – Fast and simple – Easy to visualize • Disadvantage – Assumes features are independent

5. Use Case: Detecting Poisonous Mushrooms Expert determines edibility based on cap shape, odor, gill spacing, stalk root, veil type, veil color, ring type, spore print color, habitat, gill size, stalk shape, … many more…. for 8,124 cases https://www.pinterest.com/pin/239253798926772284

6. Bayes’ Rule P(Edible | X) = P(X | Edible) P(Edible) / P(X) Where X is Odor = none and Spore print color = chocolate and Ring type = evanescent Bayes Rule Demo

7. Understand model with Visualization Everything you see is based on counts

8. How the Model Makes Predictions Multiplies conditional probabilities to come up with a posterior probability

9. How the Model Makes Predictions Multiplies conditional probabilities to come up with a posterior probability

10. Limitations of Spark Naïve Bayes Out-of-the-box version is only for document classification! Document Classification Approach • Each feature represents a word • For each feature, determine frequency for each class More General Approach • Each feature is a column • For each value of each feature, determine class distribution =* …** Prediction: Not Spam loan viagra zebra =** Prediction: Poisonousevanescent none chocolate Ring Type: Odor: SP Color: spam not spam edible poisonous

11. Limitations of Spark Naïve Bayes All columns must be non-negative integers – But what if there are nulls? – But what if there are strings? – But what if there are floating point values? – But what if there are dates? – What if target is continuous?

12. How to Handle String Columns? • First replace Nulls with a special Null value • Use StringIndexers to map string values to integer indices • Lastly, use IndexToString transformer to restore the predicted value back to a string.

13. How to Handle Continuous Columns? • Use MDLP Discretization to intelligently bin continuous (number or date) columns with respect to the target – Entropy based binning makes distributions of adjacent bins as different as possible • Replace Nulls with NaN so they can be in a separate bin.

14. Example: Job Type from Application

15. MDLP applied to a continuous column MDLP Binning 30 Uniform Bins HistogramView

16. MDLP applied to continuous column MDLP Binning 30 Uniform Bins SpinogramView

17. MDLP Binning of Continuous Features Splits seek to maximize the information, as measured by entropy Recursively split bins until – Information gain is too small – Bins are getting too small (avoids overfitting) Open Source https://github.com/sramirez/spark-MDLP-discretization

18. What if continuous Target? Automatically bin a continuous target into 3 equal weight bins

19. Mineset Demo

20. Pixel-Perfect Rendering Anti-aliased rendering Pixel-perfect rendering

21. Pixel-Perfect Rendering Anti-aliased rendering Pixel-perfect rendering

22. Conclusion • Spark Naïve Bayes classifier can be applied to any data as long as some modifications made • MDLP is useful for binning continuous features • Visualization of model provides insight, trust, and ability to answer what If questions

23. https://cloud.esi-group.com/analytics Barry Becker - bbe@esi-group.com

Visualization of Enhanced Spark Induced Naive Bayes Classifier with Barry Becker

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Visualization of Enhanced Spark Induced Naive Bayes Classifier with Barry Becker