Categorical Variables  Python for Integrated Circuits   An Online Book  

Python for Integrated Circuits http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= The OneHotEncoder() performs one hot encoding. One hot encoding consists in replacing the categorical variable by a group of binary variables which take value 0 or 1, to indicate if a certain category is present in the observation. The binary variables are also known as dummy variables. Machine learning methods such as logistic regression, SVM with a linear kernel, and so on, will often require that categorical variables be converted into dummy variables. For example, a single feature Vehicle would be converted into three features, Cars, Trucks, and Pickups, one for each category in the categorical feature. The common ways to preprocess categorical features are: Onehotencoding is a process by which categorical variables are converted into a form that could be provided to neural networks to do a better job in prediction. tf.feature_column.categorical_column_with_identity offers the best way to encode categorical data that is already indexed, i.e. has integers in [0N]. Note that decision trees can be used for both classification and regression tasks, and they are capable of handling both categorical and numerical variables. ============================================ Preprocessing categorical features. code:


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