2. About Kaggle
Biggest platform for competitive data science in the
world
Currently 500k + competitors
Great platform to learn about the latest techniques and
avoiding overfit
Great platform to share and meet up with other data
freaks
3. Approach
Get a good score as fast as possible
Using versatile libraries
Model ensembling
4. Get a good score as fast as
possible
Get the raw data into a universal format like SVMlight or
Numpy arrays.
Failing fast and failing often / Agile sprint / Iteration
Sub-linear debugging:
“output enough intermediate information as a
calculation is progressing to determine before it
finishes whether you've injected a major defect or
a significant improvement.” Paul Mineiro
7. General Strategy
Try to create “machine learning”-learning algorithms with optimized
pipelines that are:
Data agnostic (Sparse, dense, missing values, larger than memory)
Problem agnostic (Classification, regression, clustering)
Solution agnostic (Production-ready, PoC, latency)
Automated (Turn on and go to bed)
Memory-friendly (Don’t want to pay for AWS)
Robust (Good generalization, concept drift, consistent)
8. First Overview I
Classification? Regression?
Evaluation Metric
Description
Benchmark code
“Predict human activities based on their smartphone usage. Predict
if a user is sitting, walking etc.” - Smartphone User Activity Prediction
Given the HTML of ~337k websites served to users of
StumbleUpon, identify the paid content disguised as real content. -
Dato Truly Native?
10. First Overview III
Data size?
Dimensionality?
Number of train samples & test samples?
Online or offline learning?
Linear problem or non-linear problem?
Previous competitions that were similar?
11. Branch
If: Issues with the data -> Tedious clean-up
Join JSON tables, Impute missing values, Curse Kaggle
and join another competition
Else: Get data into Numpy arrays, we want:
X_train, y, X_test
12. Local Evaluation
Set up local evaluation according to competition metric
Create a simple benchmark (useful for exploration and
discarding models)
5-fold stratified cross-validation usually does the trick
Very important step for fast iteration and saving submissions,
yet easy to be lazy and use leaderboard.
Area Under the Curve, Multi-Class Classification
Accuracy
13. Data Exploration
Min, Max, Mean, Percentiles, Std, Plotting
Can detect: leakage, golden features, feature
engineering tricks, data health issues.
Caveat: At least one top 50 Kaggler used to not look at
the data at all:
“It’s called machine learning for a reason.”
14. Feature Engineering I
Log-transform count features, tf-idf transform text features
Unsupervised transforms / dimensionality reduction
Manual inspection of data
Dates -> day of month, is_holiday, season, etc.
Create histograms and cluster similar features
Using VW-varinfo or XGBfi to check 2-3-way interactions
Row stats: mean, max, min, number of NA’s.
15. Feature Engineering II
Bin numerical features to categorical features
Bayesian encoding of categorical features to likelihood
Genetic programming
Random-swap feature elimination
Time binning (customer bought in last week, last month, last year …)
Expand data (Coates & Ng, Random Bit Regression)
Automate all of this
16. Feature Engineering III
Categorical features need some special treatment
Onehot-encode for linear models (sparsity)
Colhot-encode for tree-based models (density)
Counthot-encode for large cardinality features
Likelihood-encode for experts…
18. Algorithms II
There is No Free Lunch in statistical inference
We show that all algorithms that search for an extremum of a cost
function perform exactly the same, when averaged over all possible
cost functions. – Wolpert & Macready, No free lunch theorems for search
Practical Solution for low-bias low-variance models:
Use prior knowledge / experience to limit search (Let algo’s play to their
known strengths for particular problems)
Remove or avoid their weaknesses
Combine/Bag their predictions
19. Random Forests I
A Random Forest is an ensemble of decision trees.
"Random forests are a combination of tree
predictors such that each tree depends on the
values of a random vector sampled
independently and with the same distribution for
all trees in the forest. […] More robust to noise -
“Random Forest" Breiman
20. Random Forests II
Strengths
Fast
Easy to tune
Easy to inspect
Easy to explore data with
Good Benchmark
Very wide applicability
Can introduce randomness / Diversity
Weaknesses
Memory Hungry
Popular
Slower for test time
21. GBM I
A GBM trains weak models on samples that previous
models got wrong
"A method is described for converting a weak
learning algorithm [the learner can produce an
hypothesis that performs only slightly better
than random guessing] into one that achieves
arbitrarily high accuracy." - “The Strength of Weak
Learnability" Schapire
22. GBM II
Strengths
Can achieve very good results
Can model complex problems
Works on wide variety of
problems
Use custom loss functions
No need to scale data
Weaknesses
Slower to train
Easier to overfit than RF
Weak learner assumption is
broken along the way
Tricky to tune
Popular
23. SVM I
Classification and Regression using Support Vectors
"Nothing is more practical than a good theory."
‘The Nature of Statistical Learning Theory’, Vapnik
24. SVM II
Strengths
Strong theoretical guarantees
Tuning regularization parameter
helps prevent overfit
Kernel Trick: Use custom kernels,
turn linear kernel into non-linear
kernel
Achieve state-of-the-art on
select problems
Weaknesses
Slower to train
Memory heavy
Requires a tedious grid-search
for best performance
Will probably time-out on large
datasets
25. Nearest Neighbours I
Look at the distance to other samples
"The nearest neighbor decision rule assigns to
an unclassified sample point the classification of
the nearest of a set of previously classified
points." ‘Nearest neighbor pattern classification’, Cover
et. al.
27. Perceptron I
Update weights when wrong prediction, else do nothing
The embryo of an electronic computer that [the
Navy] expects will be able to walk, talk, see,
write, reproduce itself and be conscious of its
existence. ‘New York Times’, Rosenblatt
28. Perceptron II
Strengths
Cool / Street Cred
Extremely Simple
Fast / Sparse updates
Online Learning
Works well with text
Weaknesses
Other linear algo’s usually
beat it
Does not work well on
average
No regularization
29. Neural Networks I
Inspired by biological systems (Connected neurons firing
when threshold is reached)
Because of the "all-or-none" character of nervous
activity, neural events and the relations among
them can be treated by means of propositional
logic. […] for any logical expression satisfying
certain conditions, one can find a net behaving in
the fashion it describes. ‘A Logical Calculus of the
Ideas Immanent in Nervous Activity’, McCulloch & Pitts
30. Neural Networks II
Strengths
The best for images
Can model any function
End-to-end Training
Amortizes feature
representation
Weaknesses
Can be difficult to set up
Not very interpretable
Requires specialized
hardware
Underfit / Overfit
31. Vowpal Wabbit I
Online learning while optimizing a loss function
We present a system and a set of techniques for
learning linear predictors with convex losses on
terascale datasets, with trillions of features,
billions of training examples and millions of
parameters in an hour using a cluster of 1000
machines. ‘A Reliable Effective Terascale Linear
Learning System’, Agarwal et al.
32. Vowpal Wabbit II
Strengths
Fixed memory constraint
Extremely fast
Feature expansion
Difficult to overfit
Versatile
Weaknesses
Different API
Manual feature engineering
Loses against boosting
Requires practice
Hashing can obscure
34. Ensembles I
Combine models in a way that outperforms individual
models.
“That’s how almost all ML competitions are won” -
‘Dark Knowledge’ Hinton et al.
Ensembles reduce the chance of overfit.
Bagging / Averaging -> Lower variance, slightly lower bias
Blending / Stacking -> Remove biases of base models
36. Stacked Generalization I
Train one model on the predictions of another model
A scheme for minimizing the generalization error rate of
one or more generalizers. Stacked generalization works
by deducing the biases of the generalizer(s) with
respect to a provided learning set. This deduction
proceeds by generalizing in a second space whose
inputs are (for example) the guesses of the original
generalizers when taught with part of the learning set
and trying to guess the rest of it, and whose output is
(for example) the correct guess. - ‘Stacked Generalization’,
Wolpert
38. Stacked Generalization III
Using weak base models vs. using strong base models
Using average of out-of-fold predictors vs. One model
for testing
One can also stack features when these are not
available in test set.
Can share train set predictions based on different folds
39. StackNet
We need to go deeper:
Splitting node: x1 > 5? 1 else 0
Decision tree: x1 > 5 AND x2 < 12?
Random forest: avg ( x1 > 5 AND x2 < 12?, x3 > 2? )
Stacking-1: avg ( RF1_pred > 0.9?, RF2_pred > 0.92? )
Stacking-2: avg ( S1_pred > 0.93?, S2_pred < 0.77? )
Stacking-3: avg ( SS1_pred > 0.98?, SS2_pred > 0.97? )
40. Bagging Predictors I
Averaging submissions to reduce variance
"Bagging predictors is a method for generating
multiple versions of a predictor and using these
to get an aggregated predictor." - "Bagging
Predictors". Breiman
41. Bagging Predictors II
Train models with:
Different data sets
Different algorithms
Different features subsets
Different sample subsets
Then average / vote aggregate these
42. Bagging Predictors III
One can average with:
Plain average
Geometric mean
Rank mean
Harmonic mean
KazAnova’s brute-force weighted averaging
Caruana’s forward greedy model selection
43. Brute-Force Weighted
Average
Create out-of-fold predictions for train set for n models
Pick a stepsize s, and set n weights
Try every possible weight with stepsize s
Look which set of n weights improves the train set score
the most
Can do in cross-validation-style manner for extra
robustness.
44. Greedy forward model
selection (Caruana)
Create out-of-fold predictions for the train set
Start with a base ensemble of 3 best models
Loop: Add every model from library to ensemble and pick 4
models that give best train score performance
Using place-back of models, models can be picked multiple times
(weighing them)
Using random subset selection from library in loop avoids
overfitting to single best model.
45. Automated Stack ’n Bag I
Automatically train 1000s of models and 100s of
stackers, then average everything.
“Hodor!” - Hodor
46. Automated Stack ’n Bag II
Generalization
Train random models, random parameters, random data set transforms,
random feature sets, random sample sets.
Stacking
Train random models, random parameters, random base models, with and
without original features, random feature sets, random sample sets.
Bagging
Average random selection of Stackers and Generalizers. Either pick best
model, or create more random bags and keep averaging, ‘till no increase.
47. Automated Stack ’n Bag III
Strengths
Wins Kaggle competitions
Best generalization
No tuning
No selection
No human bias
Weaknesses
Extremely slow
Redundant
Inelegant
Very complex
Bad for environment
48. Leakage I
“The introduction of information about the data
mining target, which should not be legitimately
available to mine from.” - ‘Leakage in Data Mining:
Formulation, Detection, and Avoidance’, Kaufman et.
al.
“one of the top ten data mining mistakes” -
‘Handbook of Statistical Analysis and Data Mining
Applications.’, Nisbet et. al.
49. Leakage II
Exploiting Leakage:
In predictive modeling competitions: Allowed and
beneficial for results
In Science and Business: A very big NO NO!
In both: Accidental (Complex algo’s find leakage
automatically, or KNN finds duplicates)
50. Leakage III
Dato Truly Native?
This task suffered from data collection leakage:
Dates and certain keywords (Trump) were indicative, and generalized
to private LB (but not generalize to future data).
Smartphone activity prediction
This task had not enough randomization (order of samples in train and
test set was indicative)
Could manually change predictions, because classes were clustered.
51. Winning Dato Truly Native? I
Invented StackNet
“Data science is a team sport”: it helps to join up with #1 Kaggler :)
We used basic NLP: Cleaning, lowercasing, stemming, ngrams, chargrams, tf-
idf, SVD.
Trained a lot of different models on different datasets.
Started ensembling in the last 2 weeks.
Doing research and fun stuff, while waiting for models to complete.
XGBoost the big winner (somewhat rare to use boosting for sparse text)
53. Winning Smartphone
Activity Prediction I
Prototyped Automated Stack ’n Bag (Kaggle Killer).
Let computer run for two days
Automatically inferred feature types
Did not look at the data
Beat very stiff competition
55. General strategy
Being #1 during competition sucks.
Team up
Go crazy with ensembling
Do not worry so much about replication that it freezes progress
Check previous competitions
Be patient and persistent (dont run out of steam)
Automate a lot
Stay up-to-date with State-of-the-art algorithms and tools
56. Complexity vs. Practicality I
Most Kaggle winner models are useless for production. It’s about
hyper-optimization. Top 10% probably good enough for business.
But what if we could use some Top 1% principles from Kaggle
models for business?
1-5% increase in accuracy can matter a lot!
Batch jobs allow us to overcome latency constraints
Ensembles are technically brittle, but give good generalization.
Leave no model behind!
58. Future
Use re-usable holdout set
Use contextual bandits for training the ensemble
Find more models to add to library
Ensemble pruning / compression
Interpretable black box models
