Machine learning has
come into recognition in recent years.
learning has been a buzzword. Machine learning is a category of artificial
intelligence which let software application to be rigorous to anticipate result
easily. The output value is predicted within adequate range with the help of
input data through algorithms and statistical analysis. This paper describes
various learning algorithms along with the decision tree technique which is
classification algorithm under supervised learning. In decision trees,
depending on parameters the data is divided constantly. Based on various input
variable the objective is to predict target variables with the help of decision
tree algorithm. Decision tree features and metrics are described. The usage of
the decision tree in various fields is explained. Further, advantages and
limitations of decision trees are listed.
learning, supervised learning, decision trees
programming data and program are given as inputs to the computer and after
processing output is known. Machines are made learn by providing data and
output in order to come up with the program from the computer as shown in fig.
Fig. 1 Structure of
Traditional Programming and Machine Learning
The idea of making
machines learn came up from different fields such as statistics, psychology,
biology, and computer science. Artificial intelligence algorithms are
considered as a fundamental to make a machine learn and work intelligently and
effectively depending upon situations. The motive behind making machines learn
is to come up with the best prediction or outcome. Machines are programmed in
such a manner that they produce an output learned from past experiences 1.
Among different objectives of machine learning, the important one is to find a
solution for a given problem by instructing computer for using the data or
previous actions. To yield knowledge from immense dispersed data is collected
over and over. The collected data thus help organizations to come up with new
outcomes which benefit their business along with prevailing market interest.
Large data is collected from different platforms such as social networking
sites, stocks trading, e-commerce sites, surveys 2.
The foremost step is to
formulate huge data accurately. Thus decision trees are required to come up
with useful data. The efficient working of decision trees is on discrete data
but it also works on categorical data. As decision trees classify data from previous
information it falls in supervised learning technique. To predict future events
supervised learning constructs algorithm help in the production of ordinary
patterns and assumptions that takes place with the help of external instances
provided. For a learning algorithm to be
supervised training a set of data is provided including the precise known
output. To provide improved accuracy for classification of data the
applications of real-world decision trees as considered as finest algorithm 3.
The comparison of a decision tree has been made effectively with human experts
and computerized data. These trees have turned out to be a profitable tool for
the depiction, grouping, and generalization of information. Working on building
decision trees from existing data exists in different disciplines, for example,
artificial neural network, machine learning, the theory of decision making and