Imputáció

Imputation is a statistical technique used to fill in missing data in a dataset. It involves estimating the missing values based on the available data. This process is crucial in data analysis and machine learning, as missing data can lead to biased results or reduced statistical power. Imputation methods can be broadly categorized into three types: mean imputation, median imputation, and mode imputation. Mean imputation replaces missing values with the mean of the observed data, while median imputation uses the median. Mode imputation, on the other hand, replaces missing values with the most frequent value in the dataset. More sophisticated methods include regression imputation, where missing values are predicted using a regression model, and multiple imputation, which generates multiple plausible values for each missing data point, accounting for the uncertainty in the imputed values. The choice of imputation method depends on the nature of the data and the specific requirements of the analysis. It is important to note that imputation should be done carefully to avoid introducing bias or distorting the underlying relationships in the data.