Which technique is used to reduce the impact of outliers in regression analysis? Winsorization Data transformation Cross-validation Regularization

All of the mentioned techniques can be used to reduce the impact of outliers in regression analysis.

1. Winsorization: This technique involves changing the extreme values in the statistical data to reduce the effect of possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895–1951). The distribution of many statistics can be heavily influenced by outliers. A typical strategy is to set all outliers to a specified percentile of the data; for example, a 90% winsorization would see all data below the 5th percentile set to the 5th percentile, and data above the 95th percentile set to the 95th percentile.

2. Data Transformation: This is a process that is used to convert data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data.

3. Cross-validation: This is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in reference to the model, such as k=10 becoming 10-fold cross-validation.

4. Regularization: This is a technique used to prevent overfitting in your machine learning model. Overfitting happens when your model learns both dependencies in data and random fluctuations. In other words, a model learns the existing data too well. Complex models, which have many features or terms, are often prone to overfitting. Regularization adds a penalty on the different parameters of the model to reduce the freedom of the model and in other words to avoid overfitting. The penalty term discourages learning a more complex or flexible model, so as to avoid the risk of overfitting.