Logistic regression (machine learning)
Citation, DOI, disclosures and article data
Citation:
Seah J, Murphy A, Hapugoda S, Logistic regression (machine learning). Reference article, Radiopaedia.org (Accessed on 14 Dec 2024) https://doi.org/10.53347/rID-57190
Permalink:
rID:
57190
Article created:
11 Dec 2017,
Jarrel Seah
Disclosures:
At the time the article was created Jarrel Seah had no recorded disclosures.
View Jarrel Seah's current disclosures
Last revised:
Disclosures:
At the time the article was last revised Andrew Murphy had no recorded disclosures.
View Andrew Murphy's current disclosures
Revisions:
4 times, by
3 contributors -
see full revision history and disclosures
Sections:
Tags:
Logistic regression in machine learning is a classification model which predicts the probabilities of binary outcomes, as opposed to linear regression which predicts actual values.
Logistic regression outputs are constrained between 0 and 1, and hence is a popular simple classification method for predicting whether or not a particular disease is present.
Incoming Links
Related articles: Artificial intelligence
- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- natural language processing
- machine learning (overview)
- visualising and understanding neural networks
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- dimensionality reduction
- scaling
- centring
- normalisation
- principal component analysis
- training, testing and validation datasets
- augmentation
- loss function
-
optimisation algorithms
- ADAM
- momentum (Nesterov)
- stochastic gradient descent
- mini-batch gradient descent
-
regularisation
- linear and quadratic
- batch normalisation
- ensembling
- rule-based expert systems
- glossary
- activation function
- anomaly detection
- automation bias
- backpropagation
- batch size
- computer vision
- concept drift
- cost function
- confusion matrix
- convolution
- cross validation
- curse of dimensionality
- dice similarity coefficient
- dimensionality reduction
- epoch
- explainable artificial intelligence/XAI
- feature extraction
- federated learning
- gradient descent
- ground truth
- hyperparameters
- image dataset normalisation
- image registration
- imputation
- iteration
- jaccard index
- linear algebra
- noise reduction
- normalisation
- R (Programming language)
- radiomics quality score (RQS)
- Python (Programming language)
- segmentation
- semi-supervised learning
- synthetic and augmented data
- overfitting
- underfitting
- transfer learning
Unable to process the form. Check for errors and try again.