● mastered · kind algorithm · level 4 · 30h
- Implementa: Supervised Learning
- Requiere: Linear Algebra · Statistics
Fits a linear map by least squares — the simplest supervised baseline. Everything more complex is measured against how much it beats it.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of “features” is related to that of explanatory variables used in statistical techniques such as linear regression.
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- Implementa: Supervised Learning
- Requiere: Linear Algebra · Statistics