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Update GeneralMLPrep.md

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`@@ -45,7 +45,7 @@ DEINR (pronounced as "Diner") : Data; Entropy; InformationGain ; NodeSeletion; R`
`45`	`45`	`* Entropy Calculation: Calculate the entropy of the target variable and predictor attributes to measure impurity.`
`46`	`46`	`* Information Gain: Determine the information gain for each attribute to identify which feature best splits the data.`
`47`	`47`	`* Node Selection: Choose the attribute with the highest information gain as the root node.`
`48`		`-* Recursive Splitting: Repeat this process recursively for each branch until all branches are finalized or a stopping criterion is met (e.g., maximum depth or minimum samples per leaf)`
	`48`	`+* Recursive Splitting: Repeat this process recursively for each branch until all branches are finalized or a stopping criterion is met (e.g., maximum depth or minimum samples per leaf)`
`49`	`49`
`50`	`50`	`Advantages:`
`51`	`51`	`==========`

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