Q5: Why might perfect separation be impossible in some datasets?

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Other ques from: Ch.1 from the 'Hundred-Page Machine Learning Book'

Why might perfect separation be impossible in some datasets?

Perfect separation might be impossible in some datasets because:

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1️⃣ Overlapping Classes

• The features for different classes may overlap in the feature space.

• Example: Two groups of points mixed together without a clear boundary.

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2️⃣ Noise in Data

• Random variations in measurements can cause points to appear in unexpected places.

• Example: Sensor errors or random fluctuations.

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3️⃣ Labeling Errors

• Some data points may be assigned the wrong label during data collection.

• This creates contradictions that no decision boundary can perfectly separate.

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4️⃣ Outliers

• Extreme values can disrupt the separation, even if the majority of the data is separable.

• Example: One mislabeled point far away from its class cluster.

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5️⃣ Insufficient Features

• The chosen features might not fully capture the differences between classes.

• Without the right information, no model can separate them perfectly.

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If you want, I can show you a visual example where overlapping data makes perfect separation impossible. Would you like me to prepare that?

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