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# Learning: the hows and whys of machine learning
Liam Wiltshire
https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
## Overivew
Charge backs
## Supervised learning
Training data
Learning functions
Categorisation / Classification
Regression - Where do we sit on a line
## Naive Bayes classifier
Standardise words
- Un pluralise
- Un gender
- Un tense
- etc
More data == better
## Tokenisation
https://en.wikipedia.org/wiki/Benford%27s_law
https://php-ml.readthedocs.io
Unique tokens for each unique context
## Imbalanced data
One category has more database
99% data not charge back
Just being accurate, not very helpful
- Started by flagging 100% as fine.
- Need to collect more data, change methods, resample data
## Understand data
- context
- Common data vs specific data
- Continuous vs discrete data
## KNN
K Nearest Number
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
- Distances
- less sensitive to imbalance
- Keep K odd (no draws)
## Handling nominal data
Binary
- Increase amounts of dimensions
- normalisation required
- equal scales
## Contextless data is meaningless
Is it normal?
## Next to try
Weighting
Different dimensions
Change K value (was 3NN)
Remove outliers
Diff distance function
weighted distance
# Useful links
https://en.wikipedia.org/wiki/Benford%27s_law
https://php-ml.readthedocs.io
https://liam-wiltshire.github.io/talks/?talk=machinelearning&conference=phpuk
https://joind.in/event/php-uk-conference-2019/learning-the-hows-and-whys-of-machine-learning
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
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