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Ch.16 (blueprint (the model blueprints addressed at the start of this…

- - - - To consider the blueprints, start by clicking on the name of the XGBoost model currently ranked as #5 in the leaderboard.
      - The Blueprint pane shows the XGBoost model that did better
        than all other models with the exception of the blender models built on top of this XGBoost model and two to seven other models.
  - - - This model blueprint contains more information. In this case, categorical features are one-hot encoded, and numerical features have their missing values imputed.
      - The imputation in this model uses the median value for the feature.
  - - - it becomes clear that some algorithms, such as Support Vector Machines and some linear models will struggle with features that have different standard deviations.
      - Each feature is therefore “scaled,” which means that the mean value of the feature is set to zero and the standard deviation is set to “unit variance,” which is a fancy way to say 1.
  - - - it will shows two values, "yes" or "no", true or false
- - - - the learning curves screen shows the validation scores on the Y-axis and the percent of the available data used as the X-axis.
      - on the Y-axis, lower scores are preferable because Logloss is a 'Loss' measure