Last Updated on June 30, 2020 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 task more challenging to model, more generally referred to as the curse of dimensionality. High-dimensionality statistics and dimensionality reduction techniques are often used for data visualization. Nevertheless these techniques can be used in applied machine […]

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for short. This is a technique that comes from the field of linear algebra and can be used as a […]

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the more popular technique for dimensionality reduction in machine learning is Singular Value Decomposition, or SVD for short. This is a technique that comes from the field of linear algebra and can be used as a […]

Last Updated on August 18, 2020 Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique, providing a projection of a training dataset that best separates the examples […]

Last Updated on August 18, 2020 Datasets may have missing values, and this can cause problems for many machine learning algorithms. As such, it is good practice to identify and replace missing values for each column in your input data prior to modeling your prediction task. This is called missing data imputation, or imputing for short. A popular approach for data imputation is to calculate a statistical value for each column (such as a mean) and replace all missing values […]

Last Updated on August 28, 2020 Machine learning algorithms like Linear Regression and Gaussian Naive Bayes assume the numerical variables have a Gaussian probability distribution. Your data may not have a Gaussian distribution and instead may have a Gaussian-like distribution (e.g. nearly Gaussian but with outliers or a skew) or a totally different distribution (e.g. exponential). As such, you may be able to achieve better performance on a wide range of machine learning algorithms by transforming input and/or output variables […]

Last Updated on August 28, 2020 Numerical input variables may have a highly skewed or non-standard distribution. This could be caused by outliers in the data, multi-modal distributions, highly exponential distributions, and more. Many machine learning algorithms prefer or perform better when numerical input variables and even output variables in the case of regression have a standard probability distribution, such as a Gaussian (normal) or a uniform distribution. The quantile transform provides an automatic way to transform a numeric input […]