Table of Contents
Can I use categorical variables in neural networks?
A categorical variable is a variable whose values take on the value of labels. Machine learning algorithms and deep learning neural networks require that input and output variables are numbers. This means that categorical data must be encoded to numbers before we can use it to fit and evaluate a model.
Should I normalize neural network inputs?
Among the best practices for training a Neural Network is to normalize your data to obtain a mean close to 0. Normalizing the data generally speeds up learning and leads to faster convergence.
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.
Should I scale target variable?
Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable.
Why should input be normalized?
Applications that accept untrusted input should normalize the input before validating it. Normalization is important because in Unicode, the same string can have many different representations.
Why is hot encoding necessary?
One hot encoding makes our training data more useful and expressive, and it can be rescaled easily. By using numeric values, we more easily determine a probability for our values. In particular, one hot encoding is used for our output values, since it provides more nuanced predictions than single labels.
How do you handle categorical values?
Ways To Handle Categorical Data With Implementation
- Nominal Data: The nominal data called labelled/named data. Allowed to change the order of categories, change in order doesn’t affect its value.
- Ordinal Data: Represent discretely and ordered units. Same as nominal data but have ordered/rank.