Hits: 43

Data Cleaning in R – Fix imbalance Dataset in R

Data cleaning is an essential step in the data analysis process, as it helps to ensure that the data is accurate, consistent, and reliable. One of the most common issues that can arise during data cleaning is imbalanced data, which occurs when the number of observations in one class is significantly different from the number of observations in other classes. This can lead to problems with model performance, as well as bias in the analysis.

There are several ways to fix an imbalanced dataset in R. One of the most common is to use oversampling, which involves adding additional observations of the minority class to the dataset to balance the class distribution. Another common approach is to use undersampling, which involves removing observations from the majority class to balance the class distribution.

Another approach is to use Synthetic Minority Over-sampling Technique(SMOTE) which uses interpolation to create new synthetic samples for minority class.

A different approach is to use cost-sensitive learning. This is a way to change the weight of each sample during the training process, so that the model gives more importance to the minority class. This can be done by changing the cost function or by using a different algorithm that is designed to handle imbalanced data.

In summary, Data cleaning is an essential step in the data analysis process, as it helps to ensure that the data is accurate, consistent, and reliable. One of the most common issues that can arise during data cleaning is imbalanced data, which occurs when the number of observations in one class is significantly different from the number of observations in other classes. There are several ways to fix an imbalanced dataset in R: oversampling, undersampling, Synthetic Minority Over-sampling Technique(SMOTE) and cost-sensitive learning. These techniques can help to balance the class distribution and improve the performance of the model.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Data Cleaning in R – Fix imbalance Dataset in R.

Data Cleaning in R – Fix imbalance Dataset in R

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.