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 […]

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. The two most popular techniques for scaling numerical data prior to modeling are normalization and standardization. Normalization scales each input variable separately to the range 0-1, which is the range for floating-point values where we […]

Last Updated on August 17, 2020 Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding. In this tutorial, you will discover how to use encoding schemes for categorical machine learning data. After completing this tutorial, you will know: Encoding is a […]

Last Updated on June 30, 2020 On a predictive modeling project, machine learning algorithms learn a mapping from input variables to a target variable. The most common form of predictive modeling project involves so-called structured data or tabular data. This is data as it looks in a spreadsheet or a matrix, with rows of examples and columns of features for each example. We cannot fit and evaluate machine learning algorithms on raw data; instead, we must transform the data to […]

Last Updated on June 30, 2020 Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly specific to the project. Nevertheless, there are enough commonalities across predictive modeling projects that we can define a loose sequence of steps and subtasks that you are likely to perform. This process provides a context in which we can consider the data preparation required for the project, informed both […]

Last Updated on June 30, 2020 Predictive modeling machine learning projects, such as classification and regression, always involve some form of data preparation. The specific data preparation required for a dataset depends on the specifics of the data, such as the variable types, as well as the algorithms that will be used to model them that may impose expectations or requirements on the data. Nevertheless, there is a collection of standard data preparation algorithms that can be applied to structured […]

Last Updated on August 17, 2020 Data preparation is the process of transforming raw data into a form that is appropriate for modeling. A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem referred to as data leakage, where knowledge of the hold-out test set leaks into the dataset used to train the model. This can result in an incorrect estimate of model performance when […]

Last Updated on August 17, 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 to missing data imputation is to use a model to predict the missing values. This requires a model to be created […]