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Tag Archives: R Machine-Learning

Repeated K-fold is the most preferred cross-validation technique for both classification and regression machine learning models. Shuffling and random sampling of the data set multiple… Read More
LOOCV(Leave One Out Cross-Validation) is a type of cross-validation approach in which each observation is considered as the validation set and the rest (N-1) observations… Read More
The validation set approach is a cross-validation technique in Machine learning. Cross-validation techniques are often used to judge the performance and accuracy of a machine… Read More
Quantile Regression is an algorithm that studies the impact of independent variables on different quantiles of the dependent variable distribution. Quantile Regression provides a complete… Read More
Deep Learning is a type of Artificial Intelligence or AI function that tries to imitate or mimic the working principle of a human brain for… Read More
The term Neural Networks refers to the system of neurons either organic or artificial in nature. In artificial intelligence reference, neural networks are a set… Read More
Regularization is a form of regression technique that shrinks or regularizes or constraints the coefficient estimates towards 0 (or zero). In this technique, a penalty… Read More
The word Machine Learning was first coined by Arthur Samuel in 1959. The definition of machine learning can be defined as that machine learning gives… Read More
The major challenge in designing a machine learning model is to make it work accurately on the unseen data. To know whether the designed model… Read More
Decision tree is a type of algorithm in machine learning that uses decisions as the features to represent the result in the form of a… Read More
Machine learning is a subset of Artificial Intelligence that provides a machine with the ability to learn automatically without being explicitly programmed. The machine in… Read More
Bootstrapping is a statistical method for inference about a population using sample data. It can be used to estimate the confidence interval(CI) by drawing samples… Read More
Elastic Net regression is a classification algorithm that overcomes the limitations of the lasso(least absolute shrinkage and selection operator) method which uses a penalty function… Read More
Ridge regression is a classification algorithm that works in part as it doesn’t require unbiased estimators. Ridge regression minimizes the residual sum of squares of… Read More
Lasso regression is a classification algorithm that uses shrinkage in simple and sparse models(i.e model with fewer parameters). In Shrinkage, data values are shrunk towards… Read More

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