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

How to Win a Data Science Competition

Mean encodings
ByAdmin July 11, 2021July 11, 2021

Week – 3 Mean encodings >>> How to Win a Data Science Competition: Learn from Top Kagglers 1. What can be an indicator of usefulness of mean encodings? 1 point…

Read More Mean encodings
How to Win a Data Science Competition

Graded Soft/Hard Quiz of How to Win a Data Science Competition
ByAdmin July 10, 2021

Week – 1 Graded Soft/Hard Quiz of How to Win a Data Science Competition Learn from Top Kagglers 1. Which library provides the most convenient way to perform matrix multiplication?…

Read More Graded Soft/Hard Quiz of How to Win a Data Science Competition
How to Win a Data Science Competition

Exploratory data analysis
ByAdmin July 10, 2021

Week – 2 Exploratory data analysis >>> How to Win a Data Science Competition Learn from Top Kagglers 1. Suppose we are given a data set with features XX, YY,…

Read More Exploratory data analysis
How to Win a Data Science Competition

All Assignment of How to Win a Data Science Competition
ByAdmin July 10, 2021

All Assignment of How to Win a Data Science Competition Learn from Top Kagglers Week – 1 Assignment Programming Assignment: Pandas basics Click Here To Get Week – 2 Assignment…

Read More All Assignment of How to Win a Data Science Competition
How to Win a Data Science Competition

Feature preprocessing and generation with respect to models
ByAdmin July 10, 2021

Week – 1 Feature preprocessing and generation with respect to models 1. Suppose we have a feature with all the values between 0 and 1 except few outliers larger than…

Read More Feature preprocessing and generation with respect to models
How to Win a Data Science Competition

Recap of How to Win a Data Science Competition
ByAdmin July 10, 2021

Week – 1 Recap of How to Win a Data Science Competition 1. What back propagation is usually used for in neural networks? 1 point To propagate signal through network…

Read More Recap of How to Win a Data Science Competition

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

Programming Assignment: Ensembling implementation

Week – 6

Programming Assignment: Final project

Similar Posts