Dataset

This dataset was created by Ananya Nayan

Released under Database: Open Database, Contents: © Original Authors

Contents

Logistic regression model coefficients for children (aged 10 to 15 years).

Logistic regression is a statistical method used for binary classification tasks, where the goal is to predict one of two possible outcomes. It's widely used in machine learning for tasks like spam detection, disease diagnosis, and customer churn prediction.

In logistic regression, the dependent variable (the outcome) is categorical and typically takes on two values (often represented as 0 and 1). The model works by estimating the probability that a given input belongs to a certain class, based on one or more predictor variables (which can be continuous or categorical).

Key points: Sigmoid Function: Logistic regression uses the sigmoid (or logistic) function, which maps any real-valued number to a value between 0 and 1. This is how the model outputs a probability.

The sigmoid function is given by:

𝑃 (

𝑦

1 ∣ 𝑋

)

1 1 + 𝑒 − 𝑧 P(y=1∣X)= 1+e −z

1 ​

where 𝑧 z is a linear combination of the input features:

𝑧

𝛽 0 + 𝛽 1 𝑥 1 + 𝛽 2 𝑥 2 + ⋯ + 𝛽 𝑛 𝑥 𝑛 z=β 0 ​ +β 1 ​ x 1 ​ +β 2 ​ x 2 ​ +⋯+β n ​ x n ​

Here, 𝛽 0 , 𝛽 1 , … , 𝛽 𝑛 β 0 ​ ,β 1 ​ ,…,β n ​ are the coefficients, and 𝑥 1 , 𝑥 2 , … , 𝑥 𝑛 x 1 ​ ,x 2 ​ ,…,x n ​ are the features.

Prediction: Once the model is trained, it predicts a probability 𝑃 (

𝑦

1 ∣ 𝑋 ) P(y=1∣X). A threshold (often 0.5) is used to classify the observation as belonging to one class or the other. If the probability is greater than 0.5, it predicts class 1; otherwise, it predicts class 0.

Loss Function: Logistic regression typically uses a loss function called log loss (or binary cross-entropy), which measures the difference between the predicted probabilities and the actual class labels.

Interpretability: The coefficients in logistic regression can provide insights into the relationship between each feature and the probability of the outcome. For example, a positive coefficient indicates that an increase in the corresponding feature is associated with a higher probability of the outcome being class 1.

Logistic regression is relatively simple to implement and interpret, which makes it a popular choice for many real-world classification tasks!

In This Dataset we have a total of 9 columns in which we you will se important things like in a marketing campaign like Email Clicked and Email opened or not whether they have purchased the product or not, or they visited to product page or not. Its a Simple Dataset for Logistic Regression.

Summary information from logistic regression model on the entire dataset.

Data set from PLOS ONE Article Published Entitled: Western Lowland Gorillas Signal Selectively Using Odor

Approximately 44.1 million people (about 14 percent of the U.S. population) rely on domestic wells as their source of drinking water. Unlike community water systems, which are regulated by the Safe Drinking Water Act, there is no comprehensive national program for testing domestic well water to ensure that is it safe to drink. There are many activities, e.g., resource extraction, climate change-induced drought, and changes in land use patterns that could potentially affect the quality of the ground water source for domestic wells. The Health Studies Branch (HSB) of the National Center for Environmental Health, Centers for Disease Control and Prevention, created a Clean Water for Health Program to help address domestic well concerns. The goals of this program are to identify emerging public health issues associated with using domestic wells for drinking water and begin to develop a plan to address these issues. As part of this effort, HSB in cooperation with the U.S. Geological Survey has created models to estimate the probability of arsenic occurring at various concentrations in domestic wells in the U.S. Similar work has been done by public health professionals on a state and regional basis. In the conterminous United States, we estimate that just over 2 million people are likely to have arsenic greater than 10 micrograms per liter.

Final logistic regression model.

Summary of the logistic regression model using selected variables.

To improve the information service quality of the online Traditional Chinese Medicine (TCM) community, this study investigated users’ information needs, feedback and the relationship between them. Using qualitative content analysis, the basic characteristics of users’ needs were obtained. Logistic regression was used to explore the impact of different need characteristics of feedback. The main findings are as follows: 1) Disease consultation, health preservation, professional discussion, knowledge sharing and experience description are the major 5 types of information needs in the online TCM community; 2) Some users provided TCM-related information, such as the tongue image and the TCM four diagnosis; 3) A total of 78.8% of the posts received effective feedback, and the main types of feedback were answering, discussing, inquiring and emotional supporting; 4) Providing enough information can significantly and positively affect whether needs receive effective feedback, suggesting that users can present information about their condition in as many different formats as possible when articulating their needs.

Logistic regression analysis.

Dataset

This dataset was created by Ranchantan

Released under CC0: Public Domain

Contents

Multivariable linear and logistic regression models.

Model establishment by logistic regression analysis.

aStudied from first to seventh class.bStudied from eighth to tenth class.cStudied after tenth class or pre-university education.dAwarded University degree in any speciality (Ref)e Reference category.fGap between this and the previous pregnancy (excluding primiparas mothers).gOne which is complicated by factor or factors that adversely affects the pregnancy outcome.hConception to completion of 12 weeks of gestation.iOver 12 weeks of gestation to completion of 28 weeks of gestation.jOver 28 weeks of gestation. OR, Odds Ratio; CI, Confidence Interval. *p

Observed and expected number of US counties where each invasive was present for habitat suitability ≥0.5 for both the training and test datasets.†These values are for total number of counties without a population but have a probability ≥80% based on logistic regression results.

The data are genetic assignments to upstream or downstream of Site C dam (bull trout, Arctic grayling, and rainbow trout). Columns are defined in the csv file. Also file of R code to run analysis

We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is nondifferentiable when pairs of columns in the optimization variable are equal. This encourages pairwise equality of these columns in the estimator, which corresponds to response category combination. We use an alternating direction method of multipliers algorithm to compute the estimator and we discuss the algorithm’s convergence. Prediction and model selection are also addressed. Supplemental materials for this article are available online.

The size of the Logistic Regression for Machine Learning market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The global Logistic Regression Models market is projected to reach a value of XXX million by 2033, with a CAGR of XX% during the forecast period of 2025-2033. The increasing adoption of predictive analytics and machine learning (ML) techniques in various industries is driving the market growth. Logistic regression is a statistical model used to predict the probability of a specific outcome based on a set of independent variables. It is widely used in applications such as fraud detection, customer churn prediction, and medical diagnosis. The market for Logistic Regression Models is segmented by type, application, end-user industry, and region. By type, the market is divided into binary logistic regression, multinomial logistic regression, and ordinal logistic regression. By application, the market is categorized into manufacturing, healthcare, finance, and other sectors. Key players in the market include IBM, AWS, Stata, and OARC Stats. North America and Europe are expected to be the largest regional markets for Logistic Regression Models, followed by Asia Pacific. The growing adoption of cloud-based and AI-powered analytical solutions is expected to drive market growth in these regions.