Welcome to Section five in this section, I will cover several feet methods that rely on statistical
tests to select features from a dataset.
These methods still evaluate each feature individually.
But this time, in the light of the target, we intend them to predict.
These were the methods, as I said, our statistical tests and formulations.
Specifically, I will cover selected features by using information gained.
The fish's core.
Selective features using univariate statistical tests and selecting features by determining the rock
are you see or the roots million square error, depending if it is a classification or a regression
problem.
In general, the feature selection methods from this section are performed in two steps.
In the first step, the statistical test, any of the statistical tests ranks the features based on
certain criteria, each feature is run independently of the other features based solely on their interaction
or relationship with the target.
In the second step, the features with the highest rankings are chosen to be in the classification of
regression models.
How many of the highest ranking features to select is someone arbitrary?
And usually the user sets the limit.
I would demonstrate how to put the cut off in the next lectures, the advantages of these methods are
that they run really fast and they're not computationally expensive.
The disadvantage is that they do not contemplate feature redundancy, you would have to screen for duplicated
and correlated features in previous upper tier steps.
Also, this election procedures do not contemplate feature interaction.
This is if one feature in isolation is not a good predictor, but it is when combined with the second
feature, these filter measures will not see that and will remove features on an individual assessment
basis.
The fever mess was described in the previous sections, evaluated solely each feature.
For example, if they were constant or quasi constant.
Or they compared a feature to other features in the dataset looking for duplications or correlations.
The fundamentals described here work a bit differently.
They evaluate the features in the light of the target, each feature is assessed against the target
individually and therefore it does not contemplate feature redundancy.
Think, for example, in the case of duplicated features, they were rank equally high with any of these
methods and therefore both would be selected.
So we see the importance of using these methods in combination with the others to evaluate features
altogether.