## Does smote work for categorical variables?

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SMOTE itself wouldn’t work well for a categorical only feature-set for a few reasons: It works by interpolating between different points.

## Which algorithm is best for categorical data?

Logistic Regression is a classification algorithm so it is best applied to categorical data.

**Can SVM be used for 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.

### What algorithm does smote use?

SMOTE works by utilizing a k-nearest neighbour algorithm to create synthetic data. SMOTE first start by choosing random data from the minority class, then k-nearest neighbours from the data are set.

### Does smote work for binary data?

From those 40 features I have only 1 that is numeric one (age) and all the others are binary features (yes/no for given diseases). As far as I know, SMOTE works with continuous data since it calculates the Euclidean distance among neighbors.

**Can smote be used for regression?**

The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable.

#### What data mining is most suitable for categorical variables?

BBNs can easily handle categorical variables and give you the picture of the multivariable interactions. Furthermore, you may use sensitivity analysis to observe how each variable influences your class variable.

#### Is Knn good for categorical data?

Why using 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.

**Does kNN work with categorical variables?**

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.

## Does Random Forest take categorical variables?

One advantage of decision tree based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques).

## How does smote algorithm work?

The SMOTE algorithm works as follows: You draw a random sample from the minority class. For the observations in this sample, you will identify the k nearest neighbors. You will then take one of those neighbors and identify the vector between the current data point and the selected neighbor.

**Is smote deep learning?**

We propose Deep synthetic minority oversampling technique (SMOTE), a novel oversampling algorithm for deep learning models that leverages the properties of the successful SMOTE algorithm. It is simple, yet effective in its design.

### Is the smote NC algorithm compatible with categorical features?

Notice that the very initials NC in the algorithm name mean Nominal-Continuous; as the error message clearly states, the algorithm is not designed to work with categorical (nominal) features only. To see why this is so, you have to dig a little into the original SMOTE paper; quoting from the relevant section (emphasis mine):

### Is smote-NC designed to work with categorical variables?

From X_train, col1 and col2 are my categorical features so index 0 and 1, hence I do SMOTE-NC as: ValueError: SMOTE-NC is not designed to work only with categorical features. It requires some numerical features. I wonder how one does tackle this issue given the fact that SMOTE-NC is meant to be for handling the categorical variables?

**What is the smote-NC algorithm?**

The SMOTE-NC algorithm is described below. Median computation: Compute the median of standard deviations of all continuous features for the minority class. If the nominal features differ between a sample and its potential nearest neighbors, then this median is included in the Euclidean distance computation.

#### How to oversample the mixture of categorical and numerical variables?

It’s very late but SMOTENC () is the correct method to do the oversampling for the mixture of categorical and numerical variables. Thanks for contributing an answer to Cross Validated!