EBU6504_smart_arch_notes/4-data-analytics.md

Data analytics

• Data analytics
  • Feature engineering - Definition - Sources of features - Is a part of machine learning, an iterative process - Intro - Types of feature engineering - Good feature:

Feature engineering

Definition

• The process that attempts to create additional relevant features from existing raw features, to increase the predictive power of algorithms
• Alternative definition: transfer raw data into features that better represent the underlying problem, such that the accuracy of predictive model is improved.
• Important to machine learning

Sources of features

• Different features are needed for different problems, even in the same domain

Feature engineering in ML

• Process of ML iterations:
  • Baseline model -> Feature engineering -> Model 2 -> Feature engineering -> Final
• Example: data needed to predict house price
  • ML can do that with sufficient feature
• Reason for feature engineering: Raw data are rarely useful
  • Must be mapped into a feature vector
  • Good feature engineering takes the most time out of ML

Types of feature engineering

• Indicator variable to isolate information
• Highlighting interactions between features
• Representing the feature in a different way

Good feature:

• Related to objective (important)
  • Example: the number of concrete blocks around it is not related to house prices
• Known at prediction-time
  • Some data could be known immediately, and some other data is not known in real time: Can't feed the feature to a model, if it isn't present at prediction time
  • Feature definition shouldn't change over time
  • Example: If the sales data at prediction time is only available within 3 days, with a 3 day lag, then current sale data can't be used for training (that has to predict with a 3-day old data)
• Numeric with meaningful magnitude:
  • It does not mean that categorical features can't be used in training: simply, they will need to be transformed through a process called one-hot encoding
  • Example: Font category: (Arial, Times New Roman)
• Have enough samples
  • Have at least five examples of any value before using it in your model
  • If features tend to be poorly assorted and are unbalanced, then the trained model will be biased
• Bring human insight to problem