Techniques to Improve the Performance of Supervised Learning Models

Supervised Learning can be defined as training a model with the data which includes the result itself. Many supervised learning algorithms have been found so far. There are a great number of supervised learning models. Each model performs differently and has its own merits and demerits. Many data preprocessing techniques have been found and hence the combination of various data preprocessing techniques can increase the performance of the present supervised learning models. Raw data contain a lot of noise so it cannot be fed to the learning models directly. It needs to be preprocessed using various data preprocessing techniques. The proposed work compares different combinations of various data preprocessing techniques. Comparison is done using various performance metrics and the combination of different data preprocessing is applied to different models. The work comprises categorical data handling, missing value treatment, feature scaling and feature extraction as the data preprocessing steps. The comparison gives an idea of which technique is better for which type of models. The California census 1990 data has been used for the study.