Notebook Difficulty Level: ★★★★☆
Previous Notebooks that are part of “Rock, Paper or Scissor Game – Train and Classify” module
Following Notebooks that are part of “Rock, Paper or Scissor Game – Train and Classify” module
- Rock, Paper or Scissor Game – Train and Classify [Volume 3] | Feature Selection
- Rock, Paper or Scissor Game – Train and Classify [Volume 4] | Training a Classifier
- Rock, Paper or Scissor Game – Train and Classify [Volume 5] | Performance Evaluation
| ☌ After the presentation of data acquisition conditions on the previous Jupyter Notebook , we will follow our Machine Learning Journey by specifying which features will be extracted. “Features” are numerical parameters extracted from the training data (in our case physiological signals acquired when executing gestures of “Rock, Paper or Scissor” game), characterizing objectively the training example. A good feature is a parameter that has the ability to separate the different classes of our classification system, i.e, a parameter with a characteristic range of values for each available class. |
