You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each…

Synthetic Minority Oversampling Technique (SMOTE) is typically used for class imbalance, but in a small dataset where 'missing values' can be interpreted as an insufficient number of samples, particularly in underrepresented categories, SMOTE can 'clean' the data by generating synthetic samples, making the dataset more robust for training when explicit imputation is avoided.

Replace using MICE (Multiple Imputation by Chained Equations) is an imputation method that relies on predicting missing values based on other columns, which contradicts the requirement of not applying predictors for each column.

Replace using Probabilistic PCA (Principal Components Analysis) is an imputation method that uses PCA to estimate missing values, also involving predictive modeling for imputation, which goes against the specified constraint of not requiring predictors.

Normalization is a data scaling technique that adjusts numerical feature ranges and does not address the problem of missing data values.