Medeva – 2021- Year newsletter
Dr Venugopala Rao Manneni
Learner, Practitioner & Teacher
Cohen’s Kappa coefficient
Need: In any classification analysis, if you want to measure the overall agreement between predicted and observed levels this measure will be very useful. Unlike Precision and Recall, and F1 score , Kappa co efficient considers perfect agreement between both
Read moreMeta-Analysis
Need: Whenever you want to combine & analyze several trials and also you want to estimate the summary treatment effect based on various trails or you want to obtain a finding that is beyond (single) effects found in different studies,
Read moreBonferroni Correction
Need: When conducting multiple analyses on the same dependent variable, the chance of committing a Type I error increases, thus increasing the likelihood of coming about a significant result by pure chance. To correct for this, or protect from Type
Read moreDimensionality Reduction Techniques
Need The number of input variables or features for a dataset is referred to as its dimensionality. Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. More input features often make a predictive modeling
Read moreClustering algorithms for segmentation
Need Cluster analysis is widely adopted by various applications like image processing, neuroscience, economics, network communication, medicine, recommendation systems, customer segmentation, to name a few. Additionally, clustering can be considered the initial step when dealing with a new dataset to
Read moreData-Centric AI
Need: In the data science, we are seeing the science part is getting better day by day due to developments in the algorithmic front and coming up various advanced and ensembled algorithms (Modeling centric) through which people are building models.
Read moreReporting standards for clinical prediction Models (TRIPOD)
Need: Prediction models are developed to aid health-care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their
Read moreFeature Engineering
Need: Feature engineering is one of the most important skills needed in data science and machine learning. It has a major influence on the performance of machine learning models and even the quality of insights derived during exploratory data analysis (EDA). What
Read moreFeature Selection
Need: Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline. Unnecessary features decrease training speed, decrease model interpretability, and, most importantly, decrease generalization performance on
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