Data Transformation in R – How to standardize Data in R Standardizing data in R is a way to make sure that all the variables in a dataset have the same scale. This is important because some machine learning algorithms are sensitive to the scale of the variables and can perform better when the data …
Month: October 2019
Data Transformation in R – How to scale Data in R Scaling data is a technique used to adjust the range of values in a dataset, so that they can be more easily compared and analyzed. This is often done when working with datasets that have different units of measurement, or when the range of …
Data Transformation in R – How to do pca transformation in R Principal Component Analysis (PCA) is a technique used for data transformation in order to reduce the dimensionality of a dataset. It does this by identifying patterns in the data, and then creating new features that represent those patterns. This can be useful for …
Data Transformation in R – How to do normalize transformation in R Normalization is a technique used for data transformation in order to scale the data to a specific range. It is often used to bring the data to a common scale, so that it can be more easily compared and analyzed. There are several …
Data Transformation in R – How to do ica transformation in R Independent Component Analysis (ICA) is a technique used for data transformation in order to extract independent features from a dataset. The goal of ICA is to separate a multivariate signal into independent non-Gaussian components, each representing different aspects of the original signal. In …
Data Transformation in R – How to do center transformation in R Data transformation is a technique used to change the distribution of a dataset to make it more amenable to certain statistical techniques. One such technique is centering, which is used to make the data zero-mean. Centering is a simple technique that involves subtracting …
Data Transformation in R – How to do boxcox transformation in R Data transformation is a technique used to change the distribution of a dataset to make it more amenable to certain statistical techniques. One such technique is the Box-Cox transformation, which is used to stabilize the variance of the data and make it more …
How to do Feature Selection – remove highly correlated features in R When working with a large dataset, it’s common to have features that are highly correlated with each other. These correlated features provide redundant information to the model and can negatively impact the performance. To overcome this issue, we can use feature selection techniques …
How to do Feature Selection – recursive feature elimination in R Recursive feature elimination (RFE) is a feature selection technique that recursively removes the least important features from the dataset. The goal of RFE is to select a subset of features that are most informative and relevant to the target variable, while reducing the dimensionality …
How to rank feature with importance in R – Feature selection in R Feature selection is an important step in the data analysis process, it helps to identify the most important features in a dataset and improve the performance of the model. There are many ways to rank feature importance in R, one of the …