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How can we deal with missing values in K nearest classifier?
The idea in kNN methods is to identify ‘k’ samples in the dataset that are similar or close in the space. Then we use these ‘k’ samples to estimate the value of the missing data points. Each sample’s missing values are imputed using the mean value of the ‘k’-neighbors found in the dataset.
Which of the following machine learning algorithm can be used for imputing missing values?
k-NN algorithm
k-NN algorithm can be used for imputing missing value of both categorical and continuous variables.
How do you handle categorical missing values?
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.
Which is the best algorithm to impute missing values?
In such scenarios, algorithms like k-Nearest Neighbors (kNN) can help to impute the values of missing data.
Can a kNN algorithm be used to impute missing values?
To summarize, the choice of k to impute the missing values using the kNN algorithm can be a bone of contention. Furthermore, research suggests that it is imperative to test the model using cross-validation after performing imputation with different values of k.
How to impute missing data using nearest neighbor method?
Input data, specified as a matrix. The data must have at least one row that contains no NaN because the function calculates the Euclidean distance between observation columns by using only the rows with no NaN values. Number of nearest neighbors, specified as a positive integer.
How is nearest neighbor imputation used in machine learning?
Although any one among a range of different models can be used to predict the missing values, the k-nearest neighbor (KNN) algorithm has proven to be generally effective, often referred to as “ nearest neighbor imputation .” In this tutorial, you will discover how to use nearest neighbor imputation strategies for missing data in machine learning.