Contents
Can we use binning for categorical variables?
Binning or discretization is used for the transformation of a continuous or numerical variable into a categorical feature. Binning of continuous variable introduces non-linearity and tends to improve the performance of the model. It can be also used to identify missing values or outliers.
Why do we convert categorical variables into dummy variables?
The conversion of Categorical Variables into Dummy Variables leads to the formation of the two-dimensional binary matrix where each column represents a particular category.
Why do we need bins for numerical variables?
Depending on the situation, it can lead to a better interpretation of the numerical variable, quick user segmentation or just an additional feature for building your predictive model by creating bins for the numerical variable. Binning is a popular feature engineering technique. What is Binning?
Why do we need to transform categorical variables?
We convert an n level of the categorical variable to n-1 dummy variables. There are two main reasons for it: Thanks for contributing an answer to Data Science Stack Exchange! Please be sure to answer the question. Provide details and share your research! But avoid … Asking for help, clarification, or responding to other answers.
How to convert numerical data to categorical data?
Converting numerical data into categorical requires familiarity with the dataset. For example, in the case of Titanic dataset you mention, age or class of the passenger carry predictive power but how?
What are the different types of categorical data?
These are also often known as classes or labels in the context of attributes or variables which are to be predicted by a model (popularly known as response variables). These discrete values can be text or numeric in nature (or even unstructured data like images!). There are two major classes of categorical data, nominal and ordinal.