empty or not) checked for correlations among each other and with other features and in a second step for correlations with the label before a decision on ommitting them is made. Instead of simply dropping these columns, they are converted into binary features (i.e. (see screenshot below) 3 In the right pane of OS Policies, double click/tap on the Allow Clipboard synchronization across devices policy to edit it. Many parameters are available allowing a more restrictive data cleaning where needed.įurthermore, the function klib.mv_col_handling() provides a sophisticated selection mechanism for columns with relatively many missing values. Data synchronization is the process of consolidating data across different sources, applications, and devices while maintaining consistency. 2 In the left pane of the Local Group Policy Editor, click/tap on to expand Computer Configuration, Administrative Templates, System, and OS Policies. Using this procedure, 56006 duplicate rows are identified in the subset, i.e., 56006 rows in 10 columns are encoded into a single column of dtype integer, greatly reducing the memory footprint and number of columns which should speed up model training.Īll of these functions were run with their relatively “soft” default settings. This allows us to pool and encode “carrier” and similar columns, while “tailnum” remains in the dataset. While this is unlikely, it is advised to specifically exclude features that provide sufficient informational content by themselves as well as the target column by using the “exclude” setting.Īs can be seen in *cat_plot()* the “carrier” column is made up of a few very frequent values - the top 4 values account for roughly 75% - while in “tailnum” the top 4 values barely make up 2%. While the encoding itself does not lead to a loss in information, some details might get lost in the aggregation step. These are then added to the original data what allows dropping the previously identified and now encoded columns. Specifically, the pooling is achieved by finding duplicates in subsets of the data and encoding the largest possible subset with sufficient duplicates with integers. This function “pools” columns together based on several settings. Further, klib.pool_duplicate_subsets() can be applied, what ultimately reduces the dataset to only 3.8 MB (from 51 MB originally).
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