top machine learning algorithms

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Top Machine Learning Algorithms
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Table Of Content

1. Linear regression
2. Logistic regression
3. Decision tree
4. SVM algorithm
5. Naive Bayes algorithm

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Linear regression
A machine learning algorithm based on supervised
learning is linear regression. It executes a
regression operation. Regression uses independent
variables to model a goal-accurate prediction.
It is primarily used to determine how variables
and predictions relate to one another.
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Logistic regression
One of the more often used Machine Learning
algorithms within the category of Supervised
Learning is logistic regression. A predetermined
set of independent factors predicts the
categorical dependent variable.
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Decision tree
The decision tree is the most effective and
well-liked technique for categorization and
predictions. A decision tree is a type of tree
structure that resembles a flowchart, where
each internal node represents a test on an
attribute, each branch a test result, and each
leaf node (terminal node) a class label.
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SVM algorithm
A supervised machine learning approach called
Support Vector Machine (SVM) is used for
regression and classification. Although we also
refer to regression concerns, categorization is
the most appropriate term. Finding a hyperplane
in an N-dimensional space that separates the
data points is the goal of the SVM algorithm.
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Naive Bayes algorithm
Naive Bayes is a Machine learning algorithm used
to analyze vast amounts of data it is the
suggested strategy even when working with data
that has millions of data records. It produces
excellent results for natural language
processing applications like sentimental
analysis.
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