Found 5 Articles for Dataset

Introduction In machine learning, the performance and accuracy of the model completely depend n the data that we are feeding to it, and hence it is the most influential parameter in model training and model building. Mainly while dealing with the supervised machine learning problems, we have mostly categorical and continuous variables in the dataset. There are some benefits of converting categorical variables into continuous variables. In this article, we will discuss some of the benefits of converting categorical variables to continuous variables, how it affects the model's performance, and what is the core idea behind doing so. ... Read More

Introduction Evaluating machine learning models is a crucial step to determine their performance and suitability for specific tasks. There are several evaluation approaches that can be used to gauge machine learning models, depending on the nature of the problem and the available data. Evaluation Approaches Here are some ideal evaluation approaches commonly used in machine learning: Train/Test Split This strategy aims to imitate real−world situations where the model comes upon fresh, unexplored data. We may determine how effectively a model generalizes to unobserved instances by training it on the training set and then evaluating how ... Read More

Introduction Multicollinearity, a phenomenon characterized by high correlation or linear dependence between predictor variables, poses significant challenges in regression analysis. This article explores the detrimental effects of multicollinearity on statistical models, focusing on issues such as unreliable coefficient estimates, reduced model interpretability, increased standard errors, and inefficient use of variables. We delve into the consequences of multicollinearity and discuss potential solutions to mitigate its impact. By understanding and addressing multicollinearity, researchers, and practitioners can improve the accuracy, reliability, and interpretability of regression models, enabling more robust analysis and informed decision−making. Problems with Multi−Collinearity Unreliable coefficient estimates Because ... Read More

Introduction Whenever working with time series data, it is critical to employ a cross−validation approach that accounts for the data's temporal ordering. This is because time series data displays autocorrelation, which means that the values of the data points are connected with their prior values. As a result, unlike in many other machine learning applications, the data cannot be deemed independent and identically distributed (iid). The standard k−fold cross−validation technique, which splits the data into k−folds at random and trains the model on k−1 folds before testing it on the remaining fold, is inadequate for time series data. ... Read More

Introduction Moment's big data period requires a dependable and effective approach to opting for important variables from datasets. With so numerous functions available, it can be delicate to identify which bone has the most impact on the target variable. opting for only the most important variables improves model performance, improves model interpretability, and reduces the threat of overfitting. This composition describes numerous ways to remove important variables from your dataset. We'll go through both basic statistical approaches like univariate feature selection and regularization, as well as more sophisticated techniques like PCA and feature importance ... Read More