Logistic Regression Algorithm In Machine Learning

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Logistic Regression Algorithm In Machine
Learning
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Table Of Content

1. What Is a Logistic Regression Algorithm?
2. Why Is the Logistic Regression Algorithm
   Important?
3. How Does Logistic Regression Algorithm Work?
4. Applications Of Logistic Regression Algorithm

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What Is a Logistic Regression Algorithm?
A statistical analysis technique called logistic
regression uses practical data from a data set
to predict a binary result, such as yes or no. A
logistic regression model predicts a dependent
data variable by analysing the correlation
between one or more already present independent
variables.
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Why Is the Logistic Regression Algorithm
Important?
The technique of logistic regression has been
developed significantly in the field of machine
learning. It allows machine learning algorithms
to classify incoming input based on previous
data. The algorithms accurately predict classes
within data sets when new relevant data is added.
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How Does Logistic Regression Algorithm Work?
The Supervised machine learning algorithm of
logistic regression can be used to predict the
probability of a particular class or event. It
is applied when the result is binary or
dichotomous, and the data may be linearly
separated. That means problems involving
category models are typically solved using
logistic regression.
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Applications Of Logistic Regression Algorithm
It is primarily used for forecasting, time
series modelling, prediction, and establishing
the logical relationship between variables.
Regression involves developing a graph that
connects the variables that best fit the
delivered data points. The machine learning
model can then predict the data operating in
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