## Can you impute categorical variables?

In the case of categorical variables, mode imputation distorts the relation of the most frequent label with other variables within the dataset and may lead to an over-representation of the most frequent label if the missing values are quite large.

### Which is an appropriate way of imputing the categorical variable?

One approach to imputing categorical features is to replace missing values with the most common class. You can do with by taking the index of the most common feature given in Pandas’ value_counts function.

**How do you fill missing values for categorical variables?**

There is various ways to handle missing values of categorical ways.

- Ignore observations of missing values if we are dealing with large data sets and less number of records has missing values.
- Ignore variable, if it is not significant.
- Develop model to predict missing values.
- Treat missing data as just another category.

**How do you impute categorical columns?**

Step 1: Find which category occurred most in each category using mode(). Step 2: Replace all NAN values in that column with that category. Step 3: Drop original columns and keep newly imputed columns.

## How do you impute missing categorical data in R?

How to Impute Missing Values in R

- df<-tibble(id=seq(1,10), ColumnA=c(10,9,8,7,NA,NA,20,15,12,NA),
- ColumnB=factor(c(“A”,”B”,”A”,”A”,””,”B”,”A”,”B”,””,”A”)),
- ColumnC=factor(c(“”,”BB”,”CC”,”BB”,”BB”,”CC”,”AA”,”BB”,””,”AA”)),
- ColumnD=c(NA,20,18,22,18,17,19,NA,17,23)
- 1 1 10 “A” “” NA.

### WHY IS mode imputation for categorical variables?

It distorts the distribution of the dataset. In the case of categorical variables, mode imputation distorts the relation of the most frequent label with other variables within the dataset and may lead to an over-representation of the most frequent label if the missing values are quite large.

**Can Knn be used for 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.

**How do you encode a categorical variable in Python?**

Another approach is to encode categorical values with a technique called “label encoding”, which allows you to convert each value in a column to a number. Numerical labels are always between 0 and n_categories-1. You can do label encoding via attributes . cat.

## How do you handle missing values for categorical variables in R?

Dealing with Missing Data using R

- colsum(is.na(data frame))
- sum(is.na(data frame$column name)
- Missing values can be treated using following methods :
- Mean/ Mode/ Median Imputation: Imputation is a method to fill in the missing values with estimated ones.

### How do I fill missing categorical data in pandas?

You can use df = df. fillna(df[‘Label’]. value_counts(). index[0]) to fill NaNs with the most frequent value from one column.

**What is categorical Imputer?**

The CategoricalImputer() replaces missing data in categorical variables by an arbitrary value or by the most frequent category. The CategoricalVariableImputer() imputes by default only categorical variables (type ‘object’ or ‘categorical’).

**How do I assess missing data in R?**

In R the missing values are coded by the symbol NA . To identify missings in your dataset the function is is.na() . When you import dataset from other statistical applications the missing values might be coded with a number, for example 99 . In order to let R know that is a missing value you need to recode it.

## How is mode imputation used in a categorical variable?

Mode imputation (or mode substitution) replaces missing values of a categorical variable by the mode of non-missing cases of that variable.

### How to encode and impute categorical features fast?

Based on the information we have, here is our situation: Categorical data with text that needs encoded: sex, embarked, class, who, adult_male, embark_town, alive, alone, deck1 and class1. Categorical data that has null values: age, embarked, embark_town, deck1

**Why does KNN impute all categorical features fast?**

We need to round the values because KNN will produce floats. This means that our fare column will be rounded as well, so be sure to leave any features you do not want rounded left out of the data. The process does impute all data (including continuous data), so take care of any continuous nulls upfront.

**How is mode imputation used to impute missing data?**

Mode imputation (or mode substitution) replaces missing values of a categorical variable by the mode of non-missing cases of that variable. Imputing missing data by mode is quite easy.