Be naive. not idiot.

•

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1. The document discusses leveraging the class-conditional independence assumption in machine learning models like logistic regression, decision trees, and neural networks. 2. It also discusses techniques for feature selection like filter approaches based on mutual information and wrapper approaches using greedy optimization or cross-validation. 3. Naive Bayes classifiers and extensions like selective Naive Bayes are presented, along with the "averaging trick" for soft feature selection in Naive Bayes models.

Data & Analytics

Be naive. Not idiot.
Leveraging the class-conditional independency assumption.
Sylvain Ferrandiz
21 février 2017

Logistic
Regression
XOR
MLP 4
neurons
Decision
Tree

Class-conditionnal
independency assumption
Often said simple, or naive, even idiot*
* Idiot's Bayes - not so stupid after all?, Hand, D.J., & Yu, K. (2001).
International Statistical Review. Vol 69 part 3, pages 385-399. ISSN 0306-7734.
argmax
y
p(y/x1, ..., xK) = argmax
y
p(y)
Y
k
p(xk/y)

Estimate univariate
distributions
Parametric assumption?
Nonparametric assumption?
→ Kernels
→ Binning (discretization /
grouping
→ Gaussian mixture
→ Multinomial distribution

Winning binning?
Outliers
Missing values
Stability
* MODL: a Bayes optimal discretization method for continuous attributes. Boullé, M., (2006).
Machine Learning, 65(1):131-165.
No parameter to validate
O(nlog(n))

Selective Naive Bayes
On predictive distributions and Bayesian networks. Kontkanen, P., Myllymäki, P., Silander, T., Tirri, H. & Grünwald, P. (2000).
Statistics and Computing, 10, 39-54.
sk 2 {0, 1}
argmax
y
p(y/x1, ..., xK) = argmax
y
p(y)
Y
k
p(xk/y)sk

Select features
An introduction to variable and feature selection. Guyon, I., Elisseeff, A. (2003)
Journal of machine learning research 3 (Mar), 1157-1182
Wrapper approach?
Embedded approach?
→ Greedy optimization
→ Nested subsets
→ Direct objective
optimization
Filter approach?
→ Mutual information
→ Weak learner
→ Cross-validation

Forward Feature Selection
A
B
C D
Pool of actual
candidates
Pool of future
candidates
Features
included in the
model
E
Draw
independently
Include iff the AUROCC
is improved
Keep it safe
otherwise

wk 2 [0, 1]
Soft selection
argmax
y
p(y/x1, ..., xK) = argmax
y
p(y)
Y
k
p(xk/y)wk

The averaging trick
wk =
P
s2S skp(s/d)
P
s2S p(s/d)
* A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009).
Journal of Machine Learning Research, 10:1367-1385.

The averaging trick
Explored model only
wk =
P
s2S skp(s/d)
P
s2S p(s/d)
wk =
P
s2S skp(s/d)
P
s2S p(s/d)
* A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009).
Journal of Machine Learning Research, 10:1367-1385.

The averaging trick
Nonparametric prior
wk =
P
s2S skp(s/d)
P
s2S p(s/d)
* A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009).
Journal of Machine Learning Research, 10:1367-1385.

+ -
performance
algorithm complexity
Nonparametric and stable
(bye bye cross-validation!)
It’s up to the user to find
‘composite’ features and capture
correlational relationships, but
…
It’s where the
fun is, ain’t it?Numeric / Categorical
(bye bye dummy-encoding!)
Interpretable*
*https://www.quora.com/What-makes-a-model-
interpretable/answer/Claudia-Perlich

Feature engineering
XOR
X
Y
Z = (XY > 0)
Z

Feature surfacing
Users
Sales
Web
Users
CustomerId
Firstname
Lastname
Age
Sales
CustomerId
Product
Amount
Time
Web
CustomerId
Page
Time
Users.Customer_Id
Users.Firstname
Users.Lastname
Users.Age
Outcome
Count(Sales.Product)
CountDistinct(Sales.Product)
Mean(Sales.Amount)
Sum(Sales.Amount) where Sales.Product = 'Mobile Data'
Count(Web.Page) where Day(Web.Time) in [6;7]
…

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Sylvain FERRANDIZ
Machine Learning Scientist
sfe@predicsis.ai

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办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样

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Be naive. not idiot.

1. Be naive. Not idiot. Leveraging the class-conditional independency assumption. Sylvain Ferrandiz 21 février 2017

2. Logistic Regression XOR MLP 4 neurons Decision Tree

3. Class-conditionnal independency assumption Often said simple, or naive, even idiot* * Idiot's Bayes - not so stupid after all?, Hand, D.J., & Yu, K. (2001). International Statistical Review. Vol 69 part 3, pages 385-399. ISSN 0306-7734. argmax y p(y/x1, ..., xK) = argmax y p(y) Y k p(xk/y)

4. Estimate univariate distributions Parametric assumption? Nonparametric assumption? → Kernels → Binning (discretization / grouping → Gaussian mixture → Multinomial distribution

5. Winning binning? Outliers Missing values Stability * MODL: a Bayes optimal discretization method for continuous attributes. Boullé, M., (2006). Machine Learning, 65(1):131-165. No parameter to validate O(nlog(n))

6. Selective Naive Bayes On predictive distributions and Bayesian networks. Kontkanen, P., Myllymäki, P., Silander, T., Tirri, H. & Grünwald, P. (2000). Statistics and Computing, 10, 39-54. sk 2 {0, 1} argmax y p(y/x1, ..., xK) = argmax y p(y) Y k p(xk/y)sk

7. Select features An introduction to variable and feature selection. Guyon, I., Elisseeff, A. (2003) Journal of machine learning research 3 (Mar), 1157-1182 Wrapper approach? Embedded approach? → Greedy optimization → Nested subsets → Direct objective optimization Filter approach? → Mutual information → Weak learner → Cross-validation

8. Forward Feature Selection A B C D Pool of actual candidates Pool of future candidates Features included in the model E Draw independently Include iff the AUROCC is improved Keep it safe otherwise

9. Forward Feature Selection A B C D Pool of actual candidates Pool of future candidates Features included in the model E Draw independently Include iff the AUROCC is improved Keep it safe otherwise

10. wk 2 [0, 1] Soft selection argmax y p(y/x1, ..., xK) = argmax y p(y) Y k p(xk/y)wk

11. The averaging trick wk = P s2S skp(s/d) P s2S p(s/d) * A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009). Journal of Machine Learning Research, 10:1367-1385.

12. The averaging trick Explored model only wk = P s2S skp(s/d) P s2S p(s/d) wk = P s2S skp(s/d) P s2S p(s/d) * A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009). Journal of Machine Learning Research, 10:1367-1385.

13. The averaging trick Nonparametric prior wk = P s2S skp(s/d) P s2S p(s/d) * A Parameter-Free Classification Method for Large Scale Learning. Boullé, M., (2009). Journal of Machine Learning Research, 10:1367-1385.

14. + - performance algorithm complexity Nonparametric and stable (bye bye cross-validation!) It’s up to the user to find ‘composite’ features and capture correlational relationships, but … It’s where the fun is, ain’t it?Numeric / Categorical (bye bye dummy-encoding!) Interpretable* *https://www.quora.com/What-makes-a-model- interpretable/answer/Claudia-Perlich

15. Feature engineering XOR X Y Z = (XY > 0) Z

16. Feature surfacing Users Sales Web Users CustomerId Firstname Lastname Age Sales CustomerId Product Amount Time Web CustomerId Page Time Users.Customer_Id Users.Firstname Users.Lastname Users.Age Outcome Count(Sales.Product) CountDistinct(Sales.Product) Mean(Sales.Amount) Sum(Sales.Amount) where Sales.Product = 'Mobile Data' Count(Web.Page) where Day(Web.Time) in [6;7] …

17. Let’s stay in touch! Sylvain FERRANDIZ Machine Learning Scientist sfe@predicsis.ai

Be naive. not idiot.

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