Machine learning
Imputation :
Filling in the gaps left by missing values in a dataset is known as imputation.
Imputation is a technique that can be used to address missing data before using machine learning algorithms, which is a typical problem in many datasets.
Mean imputation, median imputation, mode imputation, and regression imputation are a few techniques for filling in missing data.
-
Mean imputation replaces missing values with the mean of the non-missing values.
-
Median imputation replaces missing values with the median of the non-missing values.
-
Mode imputation replaces missing values with the mode of the non-missing values.
Regression imputation is the process of predicting missing values from the values of other variables using a regression model.
Codeblock E.1. Imputation demonstration.
Download. Download the ipynb files used here.
---- Summary ----
As of now you know all basics of Imputation.
-
Imputation is a technique used to replace missing values in a dataset with estimates based on the other available data.
-
Missing data can have a negative impact on the performance of machine learning models, so imputation is an important step in data preprocessing.
-
Imputation can be done using various methods, such as mean imputation, median imputation, and K-nearest neighbor imputation.
-
However, imputation should be performed with care, as it can introduce bias and affect the statistical properties of the data.
It is important to evaluate the performance of the imputed data before proceeding with further analysis.
etc..
________________________________________________________________________________________________________________________________
________________________________________________________________________________________________________________________________