Infilling Techniques

Infilling Techniques

Infilling techniques are a set of methods used to fill in missing or incomplete data points in a dataset. These techniques are crucial in data preprocessing, as they help improve the quality and reliability of the dataset, ultimately leading to better model performance. Infilling techniques can be broadly classified into two categories: deterministic and probabilistic methods. Deterministic methods involve using a fixed rule or function to fill in missing values, while probabilistic methods estimate the missing values based on the probability distribution of the observed data.

Deterministic Infilling Techniques

Mean Imputation

Mean imputation is a simple and widely used infilling technique that replaces missing values with the mean of the observed values for the same variable. This method is easy to implement and can help maintain the overall mean of the dataset. However, it may not be suitable for datasets with skewed distributions or outliers, as the mean can be heavily influenced by extreme values.

Median Imputation

Median imputation is similar to mean imputation, but it uses the median of the observed values instead of the mean. This method is more robust to outliers and skewed distributions, as the median is less sensitive to extreme values. However, like mean imputation, it does not take into account the relationships between variables.

Mode Imputation

Mode imputation replaces missing values with the mode of the observed values for the same variable. This method is particularly useful for categorical variables, where the mean and median may not be meaningful. However, it may not be suitable for continuous variables or datasets with multiple modes.


Interpolation is a technique that estimates missing values by fitting a curve or line through the observed data points. Linear interpolation is the simplest form, where missing values are estimated by drawing a straight line between the two nearest observed data points. More advanced interpolation methods, such as polynomial or spline interpolation, can be used to fit more complex curves through the data.

Probabilistic Infilling Techniques

Random Sampling

Random sampling is a probabilistic infilling technique that replaces missing values by randomly selecting observed values from the same variable. This method helps maintain the overall distribution of the dataset, but it may not be suitable for datasets with strong correlations between variables, as it does not take these relationships into account.

Multiple Imputation

Multiple imputation is an advanced probabilistic infilling technique that generates multiple complete datasets by filling in missing values with plausible estimates based on the observed data. These complete datasets are then analyzed separately, and the results are combined to produce a single, pooled estimate. This method helps account for the uncertainty introduced by the imputation process and can produce more accurate and reliable results than single imputation methods.

Bayesian Infilling

Bayesian infilling is a probabilistic method that uses Bayesian statistics to estimate missing values based on the observed data and prior knowledge about the data-generating process. This method can incorporate information from multiple sources, such as expert knowledge or external data, and can produce more accurate and reliable estimates than other infilling techniques.

Choosing the Right Infilling Technique

Selecting the appropriate infilling technique depends on the characteristics of the dataset and the specific problem being addressed. Factors to consider include the type of variables (continuous or categorical), the distribution of the data, the presence of outliers, and the relationships between variables. It is often helpful to experiment with multiple infilling techniques and compare their performance using cross-validation or other model evaluation metrics.

In conclusion, infilling techniques play a vital role in data preprocessing and can significantly impact the performance of machine learning models. By understanding the different methods available and their strengths and weaknesses, data scientists can make informed decisions about how to handle missing or incomplete data in their datasets.