Ensembling

ByAdmin July 11, 2021July 11, 2021

Week- 4 Ensembling >>> How to Win a Data Science Competition: Learn from Top Kagglers

Programming Assignment: Ensembling implementation

Scheme d) is more efficient from computational perspective than scheme a). That is, if a dataset is very large, scheme d) is usually preferred over scheme a).

Scheme d) is less efficient from computational perspective than scheme a). That is, if a dataset is very large, scheme a) is usually preferred over scheme d).

Scheme e) gives the validation score with the least variance, if compared to schemes a) — d).

Select fair validation schemes. The definition for the schemes can be found in the reading material.

2 points

d) Holdout scheme with OOF meta-features

e) KFold scheme with OOF meta-features

c) Meta KFold scheme with OOF meta-features

b) Meta holdout scheme with OOF meta-features

a) Simple holdout scheme

Bagging

Stacking

Boosting on trees

Weighted average

It is less prone to overfitting

It is faster to implement and to run

It usually gives better quality

[Random Forest, Extra Trees Classifier, GBDT, RGF]

[SVM, GBDT, Neural Network, kNN]

[kNN, SVM, Logistic Regression, Neural Net]

[Logistic Regression, SVM, Random Forest, Extra Trees Classifier, GBDT]

Still, sometimes it is beneficial to tune $\alpha$ and $b e t a$ independently, e.g. mix with $\alpha=0.1$ and $\beta=0.8$ works best.

However, for some metrics it never makes sense to tune $\alpha$ and $\beta$ independently. That is, searching for independent $\alpha$ and $\beta$ will never give you better results than searching for weights, constrained to be $\beta = 1 – \alpha$ . Select such metrics.

2 points

AUC

Accuracy (implemented with argmax)

LogLoss

Hinge loss

Week – 6

Programming Assignment: Final project

Click Here For Assignment

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Programming Assignment: Ensembling implementation

Week – 6

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