Decision Tree Algorithm In Machine Learning

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Decision Tree Algorithm In Machine Learning
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Table Of Content

1. What Is Decision Tree Algorithm ?
2. Why Is Decision Tree Algorithm?
3. How Does Decision Tree Algorithm Work?
4. Applications Of Decision Tree Algorithm

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What Is Decision Tree Algorithm ?
The decision tree is the non-parametric
supervised learning used for regression and
classification applications. It is organized
hierarchically and has a root node, branches,
intermediate nodes, and node of the tree.
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Why Is Decision Tree Algorithm?
In machine learning, decision trees are a
technique for structuring the algorithm. The
dataset features will be divided using a
decision tree technique and a cost function.
Trimming, a method used to remove branches that
might use pointless characteristics, is done on
the decision tree before optimizing.
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How Does Decision Tree Algorithm Work?
A decision tree is a supervised machine-learning
device that may be used to classify or forecast
data based on how queries from the past have
been answered. The method is supervised
learning, which is trained and tested using data
sets that include the required categorization.
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Applications Of Decision Tree Algorithm
Effective non-linear data management is
accomplished using decision trees. Real-world
applications of the decision tree technique
include engineers, construction management,
laws, and business. Categorical and continuous
variable decision trees are the two categories
into which decision trees may be split.
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