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Logistic Regression

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**Logistic Regression**

What is Logistic Regression?

Logistic regression is a statistical model that uses a logistic function to model the probability of a binary outcome. It's commonly used for classification tasks, predicting the likelihood of an event such as whether an email is spam or not, or whether a customer will click on an ad.

Find 1.1k organizations using Logistic Regression on Sumble →

What other technologies are related to Logistic Regression?

Logistic Regression Competitor Technologies

Linear Regression
Linear Regression is a different algorithm for regression tasks. It is often used for predicting continuous values, and can be used in similar situations to logistic regression.
decision trees
Decision trees are another machine learning algorithm that can be used for both classification and regression tasks. They can be an alternative to logistic regression for classification.
Random Forest
Random Forest is an ensemble method based on decision trees, often used for classification and regression. It can be a strong competitor to logistic regression, often providing higher accuracy.
Random Forests
Random Forests is an ensemble method based on decision trees, often used for classification and regression. It can be a strong competitor to logistic regression, often providing higher accuracy.
Gradient Boosting Trees
No summary available
Gradient Boosting Trees are another ensemble method that can be used for classification and regression. They can often achieve higher accuracy than logistic regression.
Factorization Machines
No summary available
Factorization Machines can handle various prediction tasks, including classification, by modeling feature interactions. In the classification case, it can be considered a competitor to logistic regression.
Elastic Net GLMs
No summary available
Elastic Net is a type of regularized linear regression. GLMs are a broader class of models including Logistic Regression, but other GLMs with different link functions can be competitors.
Gradient Boosting
Gradient Boosting is an ensemble method that can be used for both classification and regression. They can often achieve higher accuracy than logistic regression.
Number of organizations that mention technology
1,081
Logistic Regression
662
Linear Regression
1,136
decision trees
1,677
Random Forest
673
Random Forests
21
Gradient Boosting Trees
14
Factorization Machines
14
Elastic Net GLMs
466
Gradient Boosting
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Which job functions commonly mention Logistic Regression?

Data Scientist
0.6% of all Data Scientist jobs mention Logistic Regression
View 1.4k jobs on Sumble
Machine Learning
0.4% of all Machine Learning jobs mention Logistic Regression
View 459 jobs on Sumble
Pricing Analyst
0.4% of all Pricing Analyst jobs mention Logistic Regression
View 83 jobs on Sumble
AI Engineer
0.3% of all AI Engineer jobs mention Logistic Regression
View 264 jobs on Sumble
Data, Analytics & Machine Learning
0.2% of all Data, Analytics & Machine Learning jobs mention Logistic Regression
View 2.8k jobs on Sumble
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Which organizations are mentioning Logistic Regression?

Logistic Regression
Amazon
Retail Trade 
29 team
mention Logistic Regression
↗
 982 people
use Logistic Regression
↗
Logistic Regression
JPMorgan Chase
Finance and Insurance 
20 team
mention Logistic Regression
↗
 370 people
use Logistic Regression
↗
Logistic Regression
Citi
Finance and Insurance 
18 team
mention Logistic Regression
↗
 345 people
use Logistic Regression
↗
Logistic Regression
USAA
Finance and Insurance 
18 team
mention Logistic Regression
↗
 75 people
use Logistic Regression
↗
Logistic Regression
Visa
Finance and Insurance 
18 team
mention Logistic Regression
↗
 102 people
use Logistic Regression
↗
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