Which method is used for encoding the categorical variables

Note As of XGBoost 1.6, the feature is experimental and has limited features Starting from version 1.5, XGBoost has experimental support for categorical data available for public testing. For numerical data, the split condition is defined as \(value < threshold\), while for categorical data the split is defined depending on whether partitioning or onehot encoding is used. For partition-based splits, the splits are specified as \(value \in categories\), where categories is the set of categories in one feature. If onehot encoding is used instead, then the split is defined as \(value == category\). More advanced categorical split strategy is planned for future releases and this tutorial details how to inform XGBoost about the data type. Training with scikit-learn Interface The easiest way to pass categorical data into XGBoost is using dataframe and the scikit-learn interface like XGBClassifier. For preparing the data, users need to specify the data type of input predictor as category. For pandas/cudf Dataframe, this can be achieved by # Supported tree methods are `gpu_hist`, `approx`, and `hist`. clf = xgb . XGBClassifier ( tree_method = "gpu_hist" , enable_categorical = True ) # X is the dataframe we created in previous snippet clf . fit ( X , y ) # Must use JSON/UBJSON for serialization, otherwise the information is lost. clf . save_model ( "categorical-model.json" ) Once training is finished, most of other features can utilize the model. For instance one can plot the model...

• HOME • SOFTWARE • R • Stata • SAS • SPSS • Mplus • Other Packages • G*Power • SUDAAN • Sample Power • RESOURCES • Annotated Output • Data Analysis Examples • Frequently Asked Questions • Seminars • Textbook Examples • Which Statistical Test? • SERVICES • Remote Consulting • Services and Policies • Walk-In Consulting • Email Consulting • Fee for Service • FAQ • Software Purchasing and Updating • Consultants for Hire • Other Consulting Centers • Department of Statistics Consulting Center • Department of Biomathematics Consulting Clinic • ABOUT US Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. There are a variety of coding systems that can be used when recoding categorical variables, and which one you select depends on the comparisons that you want to make. For example, you may want to compare each level of the categorical variable to the lowest level (or any given level). In that case you would use a system called simple coding. Or you may want to compare each level to the next higher level, in which case you would want to use repeated coding. We will discuss two general types of coding and when to use them: dummy coding and effect coding. The examples in this page will use dataset called race, which has four levels (...

Introduction This is a first article in a series concentrated around feature engineering methods. Out of many different practical aspects of Machine Learning, feature engineering is at the same time one of the most important and yet the least defined one. It can be considered an art, where there are no strict rules and where creativity is the key. Feature engineering is about creating better representation of information for machine learning model. Even when using non-linear algorithms, not all interactions (relations) between variables in the dataset can be modeled, if raw data is used. This creates a need for manual inspection, processing and data manipulation. A question arises here - what about deep learning? It is supposed to minimize the need for manual processing and be able to learn a proper data representation by itself. For data such as images, speech or text, when no other ‘metadata’ is given, deep learning will perform better. In case of tabular data, nothing beats Gradient Boosted Trees methods, such as XGBoost or LightGBM. Machine learning competitions prove this - in almost every winning solution for tabular data tree-based model is the best, whereas deep learning models usually cannot achieve this good results (but they blend with trees very well ;). Basis of feature engineering is domain knowledge. This is why approach to feature engineering should be different for every dataset, depending on the problem to be solved. Still, there are some methods which ca...