How to Win a Data Science Competition

Feature extraction from text and images

ByAdmin July 10, 2021

Week – 1 Feature extraction from text and images >>> How to Win a Data Science Competition: Learn from Top Kagglers

N-grams always help increase significance of important words

N-grams can help utilize local context around each word

Levenshteining should always be applied before computing n-grams

N-grams features are typically sparse

Bag of words usually produces longer vectors than Word2vec

You do not need bag of words features in a competition if you have word2vec features.

Semantically similar words usually have similar word2vec embeddings.

Meaning of each value in BOW matrix is unknown.

We consider to use two networks: ResNet-50 with imagenet accuracy of X and VGG-16 with imageNet accuracy of Y (X < Y). Select true statements.

2 points

With one pretrained CNN model you can get only one vector of descriptors for an image

Descriptors from ResNet 50 will always be better than the ones from VGG-16 in our pipeline.

It is not clear what descriptors are better on our dataset. We should evaluate both.

For any image descriptors from the last hidden layer of ResNet-50 are the same as the descriptors from the last hidden layer of VGG-16.

Descriptors from ResNet-50 and from VGG-16 are always very similar in cosine distance.

True, False

False, True

True, True

False, False

How to Win a Data Science Competition

Graded quiz of How to Win a Data Science Competition
ByAdmin July 11, 2021

Week – 4 Graded quiz How to Win a Data Science Competition Learn from Top Kagglers 1. Which hyperparameters are first to tune in sklearn’s RandomForest? 1 point n_estimators, max_depth,…

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How to Win a Data Science Competition

Validation
ByAdmin July 10, 2021

Week – 2 Validation >>> How to Win a Data Science Competition: Learn from Top Kagglers 1. Select true statements 1 point Performance increase on a fixed cross-validation split guaranties…

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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,…

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How to Win a Data Science Competition

Metrics
ByAdmin July 11, 2021

week – 3 Metrics >>> How to Win a Data Science Competition Learn from Top Kagglers 1. Suppose we solve a binary classification task and our solution is scores with…

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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…

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How to Win a Data Science Competition

Data leakages
ByAdmin July 11, 2021

Week – 2 Data leakages >>> How to Win a Data Science Competition Learn from Top Kagglers 1. Suppose that you have a credit scoring task, where you have to…

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Week – 1 Feature extraction from text and images >>> How to Win a Data Science Competition: Learn from Top Kagglers

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