Can neural networks use categorical data?

Because neural networks work internally with numeric data, binary data (such as sex, which can be male or female) and categorical data (such as a community, which can be suburban, city or rural) must be encoded in numeric form.

Which method is used for encoding the categorical variables?

Target encoding is a Baysian encoding technique. Bayesian encoders use information from dependent/target variables to encode the categorical data. In target encoding, we calculate the mean of the target variable for each category and replace the category variable with the mean value.

Does neural network require one hot encoding?

This type of encoding creates a new binary feature for each possible category and assigns a value of 1 to the feature of each sample that corresponds to its original category. One hot encoding is a highly essential part of the feature engineering process in training for learning techniques.

Which encoding is best for categorical data?

So, binary encoding is a good choice to encode the categorical variables with less number of dimensions. In binary encoding, we convert the integers into binary i.e base 2. BaseN allows us to convert the integers with any value of the base.

What does one-hot encoding do?

A one hot encoding allows the representation of categorical data to be more expressive. Many machine learning algorithms cannot work with categorical data directly. The categories must be converted into numbers. This is required for both input and output variables that are categorical.

What are the possible challenges when performing one hot encoding on a categorical variable?

Challenges of One-Hot Encoding: Dummy Variable Trap

• VIF=1, Very Less Multicollinearity.
• VIF<5, Moderate Multicollinearity.
• VIF>5, Extreme Multicollinearity (This is what we have to avoid)