Hello. In this section, you will explore preprocessing
and featurization, as well as trying a
selection of algorithms. Automated machine
learning can apply preprocessing transformations
to your data, improving the performance
of the model. Automated machine learning
applies scaling and normalization to numeric
data automatically, helping prevent any
large-scale features from dominating training. During an automated machine
learning experiment, multiple scaling
or normalization techniques will be applied. You can choose to have
automated machine learning apply preprocessing
transformations such as missing value imputation to eliminate nulls in
the training data set, categorical encoding to convert categorical features
to numeric indicators, dropping high cardinality
features such as record IDs, feature engineering, for example, deriving
individual date parts from DateTime
features and others. For more information about the preprocessing support in
Automated Machine Learning, see what is automated machine learning from the
Microsoft website. You can find a link
to this resource from the additional readings at
the end of this lesson.