## Can SVM take categorical variables?

Among the three classification methods, only Kernel Density Classification can handle the categorical variables in theory, while kNN and SVM are unable to be applied directly since they are based on the Euclidean distances.

**Can categorical variables be used in kNN?**

KNN is an algorithm that is useful for matching a point with its closest k neighbors in a multi-dimensional space. It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data.

**Which algorithm is best for categorical data?**

NLP algorithms are usually well suited for categorical data.

### Should I normalize dummy variables?

Normalizing dummy variables makes no sense. Usually, normalization is used when the variables are measured on different scales such that a proper comparison is not possible.

**How do you create a dummy variable in R?**

To create this dummy variable, we can let “Single” be our baseline value since it occurs most often….How to Create Dummy Variables in R (Step-by-Step)

- Step 1: Create the Data. First, let’s create the dataset in R:
- Step 2: Create the Dummy Variables.
- Step 3: Perform Linear Regression.

**Can SVM work with continuous data?**

Support Vector Machine (SVM) for regression predicts continuous ordered variables based on the training data. Unlike Logistic Regression, which you use to determine a binary classification outcome, SVM for regression is primarily used to predict continuous numerical outcomes.

#### Which model is best for categorical variables?

The two most commonly used feature selection methods for categorical input data when the target variable is also categorical (e.g. classification predictive modeling) are the chi-squared statistic and the mutual information statistic.

**How does Python handle categorical data?**

The basic strategy is to convert each category value into a new column and assign a 1 or 0 (True/False) value to the column. This has the benefit of not weighting a value improperly. There are many libraries out there that support one-hot encoding but the simplest one is using pandas ‘ . get_dummies() method.

**What is MIN MAX scaling?**

Also known as min-max scaling or min-max normalization, rescaling is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data.

## Can I standardize binary variables?

It makes no sense to standardize a binary random variable. A random variable is a function that assigns a real value to an event Y:S→R. In this case 0 for failure and 1 to success, i.e. Y∈{0,1}. In the case of a proportion, this is not a binary random variable, this is a continuous variable X∈[0,1], x∈R+.