All of the encoders are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. Supported input formats include numpy arrays and pandas dataframes. If the cols parameter isn't passed, all columns with object or pandas categorical data type will be...
Categorical attributes are prevalent in many datasets used for training Machine Learning models. However, most ML models are designed to handle only numerical inputs. Therefore, converting these categorical attributes into numerical values is necessary to utilize them effect...
A hybrid neural network for input that is both categorical and quantitative The data on which a MLP (multilayer perceptron) is normally trained to approximate a continuous function may include inputs that are categorical in addition to the numeric or quantitative inputs. Examples of categorical vari...
Measuring the Effect of Categorical Encoders in Machine Learning Tasks Using Synthetic DataMost of the datasets used in Machine Learning (ML) tasks contain categorical attributes. In practice, these attributes must be numerically encoded for their use in supervised learning algorithms. Although there ...
Autoencoderscorruption of inputscategorical and numerical featuresembeddingsrepresentation learning.We focus on modelling categorical features and improving predictive power of neural networks with mixed categorical and numerical features. First, we study regu...