- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
Logistic Regression
What is Logistic Regression?
Logistic regression is the process of modeling probabilities of a specific outcome given input variables. The most common logistic regression models a binary outcome that can take two values such as healthy/not healthy, yes/no, true/false, and so on. Multinomial logistic regression can model more than two possible outcomes.
Examples of Logistic Regression
There are many practical examples of logistic regression used in everyday life such as:
Credit Card Fraud Detection: When a credit card transaction happens, a bank takes note of several things that are happening at the time of the transaction; transaction date, transaction amount, location, type of purchase, and so on. These factors are used to develop a logistic regression model to predict an outcome of whether or not the credit card transaction was fraudulent.
Email Spam Detection: Spam detection is a binary classification problem that tries to classify whether or not an email is spam. Email features such as; the sender of the email, number of typos, and frequent word occurrences like “free gift,” “offer,” “prize,” and so on, are extracted to produce a feature vector that is used to train a logistic classifier. The email then receives a score based on binary classification, and a programmed score threshold then determines if the email should be marked as spam.
Logistic Regression FAQs
What is logistic regression used for?
Logistic regression is often used to understand relationships between a dependent variable and one or more independent variables. Typical use-cases for logistic regression have a ‘yes/no’ or a ‘pass/fail’ outcome.
What is the difference between logistic regression and linear regression?
Logistic regression is used to derive the value of one dependent variable with a binary outcome. Linear regression also has one dependent variable and the outcome could depend on one or more independent variables. But the key difference being the outcome is usually a range of values. An example use-case of linear regression is predicting a range of future values of a variable across a range based on a range of input independent variable(s). It is common to represent predictions with linear regression using charts.
What are the advantages of logistic regression?
Logistic regression is easy to implement, interpret, and train. The logistic regression technique has been around since the 1970s, so it's easy for data scientists to reuse a robust implement from open-source repositories or any commercial toolkit. Since it is one of the most widely used algorithms in machine learning, it is also convenient to train data with it and do other downstream machine learning on it, e.g. deploy it, monitor the model performance, and iterate on it.
Where is logistic regression used?
Logistic regression is used to predict the categorical dependent variable. It's used when the prediction is categorical, for example, yes or no, true or false, 0 or 1.
For instance, insurance companies decide whether or not to approve a new policy based on a driver’s history, credit history and other such factors.
H2O.ai and Logistic Regression: H2O-3 includes many common machine learning algorithms, such as generalized linear modeling (linear regression, logistic regression, etc.), Naïve Bayes, principal components analysis, k-means clustering, and word2vec. H2O-3 implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning.
Logistic Regression vs Other Technologies & Methodologies
Logistic regression vs multiple regression
Logistic regression is used to determine one dependent variable that can only have two outcomes, e.g. pass/fail, yes/no. Much like classification, it is best used in situations where the outcome is binary. The model can have one or more independent variables that it depends on. The model relies on these independent variables for a certain event to occur or not. If the model has three or more outcomes, it is called multinomial logistic regression.
Multiple linear regression models are used in situations where the user needs to find values of two or more dependent variables, the outcome of which can depend on any number of independent variables. The individual dependent variables can be binomial or multinomial, i.e. it could have just two outcomes or more than two.
Logistic regression vs decision tree
Logistic regression divides a given dataset into two, assuming the plane is 2-dimensional. Examples could be yes/no, black/white, etc. Decision tree, on the other hand, divides the space into smaller areas akin to branches coming out of the main trunk. Depending upon the distribution of data, logistic regression could be seen as limiting whereas a decision tree could seem overly fitting. Selection of one or the other should be made after having a good understanding of the spread of data.
Logistic regression vs classification
Logistic regression in statistics can be thought of as a regression technique that helps a data scientist derive the probability of an event, depending upon one or more independent variables. The output variable, for a binomial logistic regression as an example, is a binary value that is derived based on a pre-configured precision-recall curve.
Classification, on the other hand, is the task of predicting a discrete class label depending upon one or more discrete input variables. There is usually no precision-recall curve involved.