Use the iris dataset, to implement the decision treeclassifier. Ensure that you explain all results and all visualizations should have clearly labeled axes and a meaningful title.Perform the following: (0 pts) Load the data and partition it into a training set and test set (3 pts) Build a decision tree classifier to predict the target (3 pts) Evaluate the predictions using the test set. (4 pts) Visualize the tree. Summarize your findings about the tree's ability to accurately predict this data. Use the iris data set from wikipedia: from sklearn.datasets import load_iris iris = load_iris () iris Iris flower data set - Wikipedia

Use the iris dataset, to implement the decision treeclassifier. Ensure that you explain all results and all visualizations should have clearly labeled axes and a meaningful title.Perform the...

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Use the iris dataset, to implement the decision treeclassifier. Ensure that you explain all results and all visualizations should have clearly labeled axes and a meaningful title.

Perform the following:

(0 pts) Load the data and partition it into a training set and test set

(3 pts) Build a decision tree classifier to predict the target

(3 pts) Evaluate the predictions using the test set.

(4 pts) Visualize the tree. Summarize your findings about the tree's ability to accurately predict this data.

Use the iris data set from wikipedia:


		from
		sklearn.datasets
		import
		load_iris
		iris
		=
		load_iris
		()
		iris


		
			Iris flower data set - Wikipedia

Answered Same Day Mar 20, 2023

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Vedant answered on Mar 21 2023

30 Votes

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Use the iris dataset, to implement the decision treeclassifier. Ensure that you explain all results and all visualizations should have clearly labeled axes and a meaningful title.Perform the...

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