68 lines
2.6 KiB
Markdown
68 lines
2.6 KiB
Markdown
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# Data analytics
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<!--toc:start-->
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- [Data analytics](#data-analytics)
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- [Feature engineering](#feature-engineering) - [Definition](#definition) -
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[Sources of features](#sources-of-features) -
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[Is a part of machine learning, an iterative process](#is-a-part-of-machine-learning-an-iterative-process) -
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[Intro](#intro) -
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[Types of feature engineering](#types-of-feature-engineering) -
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[Good feature:](#good-feature) <!--toc:end-->
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## Feature engineering
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### Definition
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- The process that attempts to create **additional** relevant features from
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**existing** raw features, to increase the predictive power of **algorithms**
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- Alternative definition: transfer raw data into features that **better
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represent** the underlying problem, such that the accuracy of predictive model
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is improved.
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- Important to machine learning
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### Sources of features
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- Different features are needed for different problems, even in the same domain
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### Feature engineering in ML
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- Process of ML iterations:
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- Baseline model -> Feature engineering -> Model 2 -> Feature engineering ->
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Final
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- Example: data needed to predict house price
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- ML can do that with sufficient feature
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- Reason for feature engineering: Raw data are rarely useful
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- Must be mapped into a feature vector
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- Good feature engineering takes the most time out of ML
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### Types of feature engineering
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- **Indicator** variable to isolate information
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- Highlighting **interactions** between features
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- Representing the feature in a **different** way
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### Good feature:
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- Related to objective (important)
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- Example: the number of concrete blocks around it is not related to house
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prices
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- Known at prediction-time
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- Some data could be known **immediately**, and some other data is not known
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in **real time**: Can't feed the feature to a model, if it isn't present
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at prediction time
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- Feature definition shouldn't **change** over time
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- Example: If the sales data at prediction time is only available within 3
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days, with a 3 day lag, then current sale data can't be used for training
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(that has to predict with a 3-day old data)
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- Numeric with meaningful magnitude:
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- It does not mean that **categorical** features can't be used in training:
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simply, they will need to be **transformed** through a process called
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one-hot encoding
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- Example: Font category: (Arial, Times New Roman)
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- Have enough samples
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- Have at least five examples of any value before using it in your model
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- If features tend to be poorly assorted and are unbalanced, then the
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trained model will be biased
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- Bring human insight to problem
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