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--- ease:machinelearning:classifier_training [2020/06/22 11:25] – s_fuyedc
+++ ease:machinelearning:classifier_training [2020/06/22 11:46] (current) – s_fuyedc
@@ Line 13: / Line 13: @@
 </code>
-The parameters that we speak of are within the two //range// constructions, namely the 9 for the max_depth and 21 for max_leaf_notes. Finding parameters that train the model to an F1 value above 0.9 can be done programmatically by increasing both values, since more depth and nodes result in a more precise model. Taking a maximum depth of 14 and 26 leaf nodes are the lowest values that reach a score of just above 0.9.
+After preparing the test- and dataset, two suitable parameters need to be decided for, determining the depth and breadth of the decision tree. The parameters that we speak of are within the two //range// constructions, namely the 9 for the max_depth and 21 for max_leaf_notes. Finding parameters that train the model to an F1 value above 0.9 can be done programmatically by increasing both values, since more depth and nodes result in a more precise model. Taking a maximum depth of 14 and 26 leaf nodes is the lowest setting to reach a score of just above 0.9.
 <code python>
 parameters = {'max_depth':range(1,14,1), 'max_leaf_nodes': range(2,26,1)}