![]() ![]() ![]() The lift measure requires that confidence measure is also present, but it can be set to arbitrarily low value.The default measures are confidence or lift, but via the Add interest measure link it is possible to also add the Lift measure. The final step in preparing the rule learning task is setting of minimum thresholds for selected interest measures. If you choose Any value, the attribute can appear with any value.If you choose fixed value, the attribute can appear in discovered rule only with the prespecified value.When an attribute is placed to the attribute palette, you have the option to set Fixed value or Any value. If there is a target attribute in the task, we typically place it to the consequent, the remaining attributes are placed into the antecedent. We use the term attribute to refer to the preprocessed datafield.īy dragging attributes to the rule pattern, you can define which attributes can appear on the left side (antecedent) and right side (consequent) of the rules. The three options for selecting data fields for preprocessing are displayed on the picture. If you want to use an attribute in a data mining task, it needs to be preprocessed. In the final step of creating the miner, you can set the name. In the next step, you can change data types for individual columns. When the data are too large for this default miner type, you can try using the Cloud-based miner.Īfter uploading the dataset, there is the option to change upload parameters, such as encoding or separator. The recommended miner is suitable for smaller datasets as it provides fastest response. This will open existing dataset including any preprocessing and discovered rules saved into the rule clipboard.Īpart from choosing the file to import, this screen allows you to set the miner (database) type. from unlimited (cloud) to limited (R) you will need to upload the dataset again. Reuse already uploaded dataset: use this option if you want to preprocess the dataset in a different way.Upload your data: EasyMiner accepts the Comma Separated Values (CSV) format.Upload data, reuse existing datasource or open minerĪfter logging in, you have three options: The tutorial will use the titanic dataset. Inspect results, select rules, export or save.Choose action: Upload data, reuse existing datasource or open miner.This tutorial will walk you through the complete data mining task with EasyMiner. ![]()
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