In the context of the Iris dataset, which of the following data preprocessing techniques is most crucial for ensuring accurate business intelligence insights when analyzing flower species classification?

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Seekh · Accepted Answer

The most crucial preprocessing step for the Iris dataset is scaling the numeric features so they all share a common range, because sepal and petal measurements have different units and magnitudes. Without scaling, a classifier might give too much weight to the feature with the largest values, distorting the decision boundaries. Normalizing or standardizing the data ensures that each attribute contributes equally to the similarity calculations used in clustering or classification. For example, if sepal length is measured in centimeters and petal width in millimeters, scaling them brings both to a comparable scale and improves the accuracy of business intelligence insights derived from the model.

In the context of the Iris dataset, which of the following data preprocessing techniques is most crucial for ensuring accurate business intelligence insights when analyzing flower species classification?

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