Machine learning interview questions now focus on both theory and real-world applications.Understanding basics like ...

Artificial intelligence models used to detect depression on social media are often biased and methodologically flawed, ...

Previous methods struggle to incorporate real-time data or account for nonlinear interactions among macroeconomic variables.

US infants lacking Bifidobacterium in their gut microbiome face significant changes in microbial composition, metabolic ...

Objective We aimed to estimate prevalence and identify determinants of hypertension in adults aged 15–49 years in Tanzania. Design We analysed cross-sectional survey data from the 2022 Tanzania ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

In the field of machine learning, linear regression can be considered a type of supervised machine learning. In this use of the method, the model learns from labeled data (a training dataset), fits ...

Various machine learning models include Naive Bayes, KNN, Random Forest, Boosting, AdaBoot, Linear Regression, and more. However, the model you must pick depends on the situation or the project ...