The first slide focuses on the Feature Filtering configured in the [Dataiku Application](article:15).

![Information Value.png](NgagFe1BfVfI)

Each bar is the Information Value of a variable with respect to the credit event target. The higher the value, the more information is contained in the variable to explain the credit event. The horizontal line corresponds to the threshold for keeping a variable. The right-hand side graph gives a sense of the distribution of information value across variables, the interesting variables are the ones situated on the right of the graph.

![Chi Square P Value.png](xaaMpDcT80Ar)

The two graphs about the Chi-Square p-value are very similar to the above except that in that case, the lower the better. Those variables with a p-value above the set threshold are likely to be independent of the target variable and therefore not interesting for the analysis. And the graph next to it shows how these p-values are distributed for our set of variables.

![Correlation Matrix.png](qyvUieEPwcxo)

The correlation matrix is not directly visible in the dashboard because its set of variables changes from one project to another. However, a link takes the user to the statistics worksheet where the correlation matrix is computed with the selected method on all numeric variables. One should focus on the brighter tiles, either red or blue, that indicate either significant positive or negative correlations. Over these blocks of correlated variables, only the most information will be kept in the following stages.


