e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021
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Figure 4: K-Nearest Neighbor The confusion Matrix of the dataset obtained from the K-Nearest Neighbors is shown below which provides all the necessary measures of the choice:
5.0 Extra Trees Classifier Extra Trees Classifier is another kind of ensemble learning technique that combine the results of multiple decorrelated trees collected in a forest to output its result. It is very similar to a Random Forest algorithm but only differs from constructing a vast number of decision trees in the forest. Each Tree in the Extra Trees Forest is built from the original training instances. Then, at each test node, each decision tree is provided with a random piece of 'k features' from the feature-set. Each decision tree must select the best available feature to split the data based on some criteria (e.g. Gini Index). This arbitrary sample of features leads to the creation of multiple de-correlated decision trees.
Figure 5: Extra Tree Classifier www.irjmets.com
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