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# Decision Tree In Machine Learning

## Understanding Decision Trees

The process of a decision tree is a method deployed in machine learning utilized for classification and regression. This technique is called a decision tree due to the way it hierarchically slices the data until each sample is individually categorized. If you imagine a tree with countless branches, you'd have a visual representation of the outcomes of a decision tree's algorithm. To get the most out of your machine learning projects, it's crucial to grasp how decision trees operate and how they're applied.

## Key Components of Decision Trees

In a decision tree, there are internal and leaf nodes symbolizing tests on attributes, and branches that signify conjunctions of facets leading to class labels. Depending on the type of query, there are three different categories of decision trees in machine learning.

## The Structural Design of a Decision Tree

A decision tree's function in machine learning is akin to a flowchart encompassing a sequence of choices and their associated outcomes. The root is the starting point, and depending on the response to the initial filtering criterion, you proceed to potential subsequent nodes. This cycle repeats until an absolute endpoint is reached. Each internal node signifies a testing or filtering principle. The outer nodes, known as "leaves," assign labels to the data point under examination. Every internal node links to the next through a series of characteristics, termed branches.

## Algorithmic Functioning

In application, a decision tree fragments the dataset into separate data units hinging on distinct criteria. Variables that are dissimilar draw divisions in the dataset. Numerous techniques exist for tree splitting, with the "recursive binary split" predominantly used. An algorithm determines the potential accuracy compromise of each imaginable split. Cost functions measure the split's cost, with different ones used for regression and classification tasks. Both functions aim to identify which branches have similar response values.

## Binary Splits and Cost Functions

For binary splits, the cost function is: sum(y-prediction)^2. For classification, the cost function is: G = sum(pk*(pk-1)). The Gini score assesses a split's effectiveness. A perfect split is when all groups formed post-split only consist of inputs from one class.