10 things I wish I knew… - ETH...
Transcript of 10 things I wish I knew… - ETH...
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10 things I wish I knew……about Machine Learning Competitions
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Introduction• Theoretical competition run-down• The list of things I wish I knew• Code samples for a running competition
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Kaggle – the platform
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Reasons to compete• Money• Fame• Learning experience• Tough challenge• Fun
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Competition run-down• Head over to kaggle.com• Read the competition description• Download the train/test set
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Preparations• Plot the data• Look at the distributions• Start simple (all-zeroes benchmark)• Make sure to optimize the correct metric• Read up on the specific propertiesà e.g. Logarithmic Loss, extremepredictionshttps://www.kaggle.com/wiki/Metrics
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Preprocessing• Replace missing values• Remove duplicates from the training set• One-Hot encode categorical features• Decide what to do with outliers• Scaling/Standardizing
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Building the model• Start with a baseline or simple modelà Random predictionsà LogisticRegressionà Decision treesà KNearestNeighbours
• Establish a cross-validation scheme
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Submit• Leaderboard score vs. local score
• Mismatch?à Check your scoring functionà Check the sample size of the public LBà Ignore the LB
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Kaggle isn’t real world ML• Trade-off:
Accuracy vs. Interpretability vs. Speed• Interpretability/speed is often more important
than accuracy• "Arrow splitting“• "Netflix Problem"http://fastml.com/kaggle-vs-industry-as-seen-through-lens-of-the-avito-competition/http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.htmlhttp://machinelearningmastery.com/building-a-production-machine-learning-infrastructure/
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1) Timing• Don’t start too early
«Beat the benchmark», sharing, motivation
• Don’t start too lateYou’ll certainly run out of time
• ~ 30 Days before the deadline
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2) Learn a tool, stick with it• Python• R• Matlab/Octave
“The grass is always greener on the other side”
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3) Make sure your result are reproducible
• Fix the seeds for algorithms that involverandomization
• Automate your pipeline• Preferably one script from input to output
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4) Make sure your result are reproducible
Examples:• Weight initialization (Neural Networks)• Data subsampling (e.g. Random Forest)
# scikit-learntrain_test_split(X, y, random_state=42)
# numpynp.random.seed(42)
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5) Don’t trust the Leaderboard• Danger of overfitting when tuning your
models according to feedback of the publicleaderboard
• Use cross-validation to estimate theperformance of your model
• Don’t, if computationally to expensiveà Train/Test split might cut it too
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6) Avoid LeakageCommon Sources• PCA• TfIdf• Imputation (Mean/Median)• Duplicate rows in the training set• Inappropriate Cross-validation Scheme
Row, Person, Time, Location
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7) Bias/Variance Trade-offHigh Variance (Overfitting)High Bias (Underfitting)
https://www.coursera.org/course/ml
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8) Think outside the box
• «Don’t get stuck in local minima»• Stop doing what you’re doing if you’re not
making significant progress• Read-up relevant papers on the problem• Explore a different model• Try more feature engineering
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9) Spend your time wisely• Feature Engineering vs. Hyper-parameter
tuning• Read up on Error Analysis• Read up on Learning Curves
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9) Improving a learning algorithm• Get more training examples (V)• Try smaller sets of features (V)• Try getting additional features (B)• Try adding polynomial features (B)• Increase regularization (V)• Decrease regularization (B)
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10) Make use of ensembling• Six bad models are usually better than one
really good model [1, 2]à KNN, SVM, NeuralNet, RF,LogisticRegression, Ridgeà Neural Nets (structurally, seed)
• Make yourself familiar with:Bagging, Boosting, Blending, Stacking[1] http://www.tandfonline.com/doi/abs/10.1080/095400996116839#.VEebN_nkcyN[2] http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf
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An example would be handy…
…right about now.
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Make use of ensembling (cont)
http://www.overkillanalytics.net/more-is-always-better-the-power-of-simple-ensembles/
True signal
Linearmodel
Non-Linearm
odel
Training data Averaged
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Working with features
• Feature selection• Feature engineeringà categoricalà numericalà textual
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Examples of feature selection/engineering
• Remove correlated features• Remove features using statistical tests
• Try pair-wise feature interactionsa*b, a-b, a+b, a/b
• Try feature transformationssqrt(a), log(a), abs(a)
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Feature engineering (categorical)
• CabinID into deck and room number‘A25’à (‘A’, 25)‘B16’à (‘B’, 16)
• Recode number of siblings to binary (family)• Decompose Dates
Year, month, dayDay of the weekDay of the month
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Feature engineering (Textual)
• Lowercase• Stemming (‘rainy’à ‘rain’)• Spelling correction
«I wsa hungray»à «I was hungry»«It’s hotttt outside»à «It’s hot outside»
• Remove stopwords• N-Grams• TfIdf, Count, Hashing
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What usually doesn’t work (for me)• Dimensionality reduction (information loss)• Feature elimination (information loss)• Tree-based methods on High-
dimensional/Sparse data (by design)
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There is always a twist• Feature engineeringà a.k.a. “Golden Features”
• How exciting is this project?à linear decay towards the end
• Removing useless/noisy features
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Dataset Trends• Datasets become larger (millions of
samples, thousands of features)• Datasets are anonymizedà Black-Box Machine Learning
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Interesting stuff to keep an eye on• Caffe, cuDNN• Vowpal Wabbit (Wee-Dub)• h2o from 0xdata• Regularized Greedy Forests• Factorization models
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55 features , 15k training samples, ~500k Test samples
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Random predictions
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Start simple: Decision tree
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A little more complex
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Let’s see what the model thinks
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Next: SVM!
What?!
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Feature scaling!
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Enough playing, let’s get real.
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73.5% accuracy?
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Class distribution
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Scale it up!
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75.489% accuracy
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Even more?
Nope, no more progress! Time to switch tactics.
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Feature Engineering
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78.212% accuracy
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One more round
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Mail [email protected]: @mattvonrohrLinkedIn: ch.linkedin.com/in/mattvonrohr/Kaggle: kaggle.com/users/8376/matt