Can KNN Imputer be used for categorical data?

For imputing missing values in categorical variables, we have to encode the categorical values into numeric values as kNNImputer works only for numeric variables. We can perform this using a mapping of categories to numeric variables.

Which method is suitable for categorical data?

Frequency tables, pie charts, and bar charts are the most appropriate graphical displays for categorical variables.

Which distance metric do we use in KNN for categorical variables?

Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.

How do categorical variables deal with missing values?

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

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

How do you impute categorical missing values?

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 get the best K in KNN in Python?

The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers.

How do you present categorical data in a table?

Presenting categorical data: key terms Frequency table – A table that displays numbers and percentages for each value of a variable. Pie chart – A simple way to show the distribution of a variable that has a relatively small number of values, or categories.

How do you summarize categorical data?

Counting on the frequency One way to summarize categorical data is to simply count, or tally up, the number of individuals that fall into each category. The number of individuals in any given category is called the frequency (or count) for that category.

What kind of distance metric is suitable for categorical variable?

Hamming distance is used to measure the distance between categorical variables, and the Cosine distance metric is mainly used to find the amount of similarity between two data points.

Does KNN handle categorical features?

It doesn’t handle categorical features. This is a fundamental weakness of kNN. kNN doesn’t work great in general when features are on different scales. This is especially true when one of the ‘scales’ is a category label.

Should I use KNN or PAM for categorical data?

If you end up deciding that some other notion of distance makes more sense (for example, something like Jaccard distance if all your data is actually binary), then you should look at “partitioning around medoids” (PAM) instead of KNN. Use one hot encoding for the categorical data.

How to perform k-level feature scaling for categorical data?

Enumerate the categorical data, give numbers to the categories, like cat = 1, dog = 2 etc. Perform feature scaling. So that the loss function is not biased to some particular features. Done, now apply the K- nearnest neighbours algorithm. Leave the numerical features unchanged. Assume for some categorical feature F, we have K levels.

How do I use the KNN method?

The KNN method is a Multiindex method, meaning the data needs to all be handled then imputed. Next, we are going to load and view our data. A couple of items to address in this block. First, we set our max columns to none so we can view every column in the dataset.