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Cluster analysis can be a powerful data-mining tool to identify discrete groups of customers, sales transactions, or types of behaviours.
One-hot encoding.This is the inverse of binning; it creates numerical features from categorical variables. One-hot encoding maps categorical features to binary representations, which are used to map the feature in a matrix or vector space. Literature often refers to this binary representation as a...
The deep model is a Dense Neural Network (DNN), a series of five hidden MLP layers of 1024 neurons, each beginning with a dense embedding of features. Categorical variables are embedded into continuous vector spaces before being fed to the DNN via learned or user-determined embeddings. What...
It is used when the dependent variable is binary or categorical. It models the probability of an event occurring by fitting a logistic function to the independent variables. The output is a probability score that can be used to classify instances into different classes. It is widely used in ...
“Categorical exemptions” are when a particular type of information or record is exempt, and “conditional exemptions” refer to exempting a record depending on the effect on a privacy right or government interest it might have. Examples of statutes incorporated into the public record law use a...
"Boosting is often recommended when you have a large number of observations in training data, and data has a mixture of numerical and categorical features or just numeric features," she said. Boosting's ability to produce more accurate predictions, personalized recommendations and improved decision-...
Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts th...
Different from classification where predicted output values are categorical, regression models predict numerical output values based on independent predictors. In regression, the objective is to help establish the relationship among those independent predictor variables by estimating how one variable impacts th...
Feature crosses -- a way to combine two or more categorical features into one. This technique is particularly useful when certain features together denote a property better than they do by themselves. There are some open sourcePythonlibraries that support feature engineering techniques, including the...