IS 4490 - Data Types and Encoding

What is a major disadvantage of one hot encoding?

It can significantly increase the number of features

What distinguishes ordinal data from regular categorical data?

Ordinal data has a meaningful order or ranking between categories

Which type of data is characterized by distinct, unordered categories and is used to classify or label items?

Categorical data

According to the text, what is a primary benefit of using one-hot encoding?

It creates a numerical representation of categorical data without implying an ordinal relationship.

Which of the following is a special case of categorical data with only two possible values, such as 1 and 0?

Binary data

We have a column with color as a feature. It has values "Red", "Blue", and "Green". What does one-hot encoding a "Color" feature result in?

Three binary columns, one for each color, where only the relevant column has a value of 1.

Categorical data has a natural numerical order that allows for mathematical operations between categories.

False

One hot encoding can imply ordinal relationships between categories, which is why it's preferred for categorical data.

False

In ordinal data, the magnitude of differences between categories is always consistent and measurable.

False

Sometimes ordinal data might be treated as numerical for convenience, especially for Likert scale responses.

True

One hot encoding creates a numerical representation while avoiding implied ordinal relationships between categories.

True

A model that predicts whether an email is spam or not is an example of a regression problem.

False

Regression is used to analyze the relationship between dependent and independent variables to make predictions.

True

Weather forecasting is an example where regression plays a crucial role.

True

In supervised learning, once a model is trained, it cannot make predictions on new, unseen data.

False