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A decision tree is drawn the wrong way up with its root at the highest. within the image on the left, the bold text in black represents a condition/internal node, supported which the tree splits into branches/ edges.
Repeat until leaves are “pure”, i.e., have example from one class, or “nearly pure”, i.e., majority of examples are from an equivalent class
A tree is made by splitting the source set, constituting the basis node of the tree, into subsets—which constitute the successor children. The splitting is predicated on a group of splitting rules supported classification features
A tree is made by splitting the source set, constituting the basis node of the tree, into subsets—which constitute the successor children. The splitting is predicated on a group of splitting rules supported classification features
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This process is repeated on each derived subset during a recursive manner called recursive partitioning. The recursion is completed when the subset at a node has all an equivalent values of the target variable, or when splitting does not add value to the predictions. This process of top-down induction of decision trees
Let’s say we've got a drag to predict whether a customer can pay his renewal premium with an insurance firm (yes/ no). Here we all know that income of customer may be a significant variable but insurance firm doesn't have income details for all customers. Now, as we all know this is often a crucial variable, then we will build a choice tree to predict customer income supported occupation, product and various other variables.
during this case, we are predicting values for continuous variable.
what is actually happening within the background? Growing a tree involves choosing which features to settle on and what conditions to use for splitting, alongside knowing when to prevent. As a tree generally grows arbitrarily, you'll get to trim it down for it to seem beautiful. Lets start with a standard technique used for splitting.
what is actually happening within the background? Growing a tree involves choosing which features to settle on and what conditions to use for splitting, alongside knowing when to prevent. As a tree generally grows arbitrarily, you'll get to trim it down for it to seem beautiful. Lets start with a standard technique used for splitting.
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Information gain is employed to make a decision about which features to separate on at each step in building the tree. Simplicity is best, so we would like to stay our tree small. To do so, at each step we should always choose the split that leads to the purest daughter nodes.
A commonly used measure of purity is named information which is measured in bits. for every node of the tree, the knowledge value "represents the expected amount of data that might be needed to specify whether a replacement instance should be classified yes or no, as long as the instance reached that node
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