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 […]

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 have a standard probability distribution. The discretization transform provides an automatic way to change a numeric input variable to have a different data distribution, which in turn can be used as input to a predictive […]

Last Updated on August 28, 2020 Recursive Feature Elimination, or RFE for short, is a popular feature selection algorithm. RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting the target variable. There are two important configuration options when using RFE: the choice in the number of features to select and the choice of the algorithm used […]

Last Updated on August 28, 2020 Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that use a weighted sum of the input, like linear regression, and algorithms that use distance measures, like k-nearest neighbors. Standardizing is a popular scaling technique that subtracts the mean from values and divides by the standard deviation, transforming the probability distribution for an input variable to a standard Gaussian (zero mean and unit variance). […]

Last Updated on August 28, 2020 Often, the input features for a predictive modeling task interact in unexpected and often nonlinear ways. These interactions can be identified and modeled by a learning algorithm. Another approach is to engineer new features that expose these interactions and see if they improve model performance. Additionally, transforms like raising input variables to a power can help to better expose the important relationships between input variables and the target variable. These features are called interaction […]

Last Updated on August 18, 2020 Test-time augmentation, or TTA for short, is a technique for improving the skill of predictive models. It is typically used to improve the predictive performance of deep learning models on image datasets where predictions are averaged across multiple augmented versions of each image in the test dataset. Although popular with image datasets and neural network models, test-time augmentation can be used with any machine learning algorithm on tabular datasets, such as those often seen […]

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 sophisticated approach involves defining a model to predict each missing feature as a function of all other features and to repeat this process […]

Last Updated on August 18, 2020 Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with real-valued input and output data, such as using the Pearson’s correlation coefficient, but can be challenging when working with numerical input data and a categorical target variable. The two most commonly used feature selection methods for numerical input data when the target variable is […]

Last Updated on August 18, 2020 Feature selection is the process of identifying and selecting a subset of input variables that are most relevant to the target variable. Perhaps the simplest case of feature selection is the case where there are numerical input variables and a numerical target for regression predictive modeling. This is because the strength of the relationship between each input variable and the target can be calculated, called correlation, and compared relative to each other. In this […]