motivation
Recently I revisited the classic Monte-Carlo fundamentals (random number generator, importance/stratified sampling, QMC, etc), and found there are some cross explanations, sometimes
confusing, about the MC estimator, so I decided to write down my own understandings.
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Data spliting, CV, and re-sampling
the logically justified approach of data processing
motivation
Two common mistakes that many machine learning theorists and practioners make are:
impute missing values and standardizing features before splitting
resampling the imbalanced data before cross-validation
Here I want to address the importance of the sequence with statistical evidence.
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No more unit tests pls
while testing is normal, anti-testing is abnormal
Life is like, you write a dozen of unit tests and no one passes.
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Machine learning with OOP
Implement OOP principles in a machine learning context
References:
Object-oriented programming for data scientists.
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Create a Python Package with __init__.py
A practical hint on Pythonn import implementation
References:
How to create a Python Package with init.py.
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