Logistic Regression

AI Terms

Logistic Regression

Definition: Logistic regression is a statistical method used for classification tasks. It models the probability of a binary outcome (e.g., 0 or 1) based on one or more predictor variables.

Detailed Description:

Logistic regression is a type of generalized linear model that uses a logistic function to map the linear combination of predictor variables to a probability between 0 and 1. The logistic function is:

p(y=1|x) = 1 / (1 + exp(-z))

where:

p(y=1|x) is the probability of the outcome being 1 given the predictor variables x.
z is the linear combination of the predictor variables.

Logistic regression is used in various applications, such as:

Spam filtering: Classifying emails as spam or not spam.
Sentiment analysis: Determining the sentiment of a text.
Medical diagnosis: Predicting the presence or absence of a disease.

Related Terms:

Classification
Machine Learning
Logistic Function
Odds Ratio
Generalized Linear Models

FAQs:

What is the difference between linear regression and logistic regression?
- Linear regression predicts a continuous numerical value, while logistic regression predicts a binary outcome.
How is logistic regression used in spam filtering?
- Logistic regression can be used to classify emails as spam or not spam based on features such as word frequency, sender address, and subject line.
What are the assumptions of logistic regression?
- Linearity, independence of errors, and absence of multicollinearity.

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