Overfitting is one of the most common concepts in data science. The situation normally occurs when the statistical data model applied fits exactly against the training data set. It thus becomes challenging for the researcher to identify the basic features of the data and how to rectify the errors that may arise during the data mining process.
More especially, the model memorizes the noise and bias during the research or data scrapping procedure. The model also becomes unfit to generalize the new data sources. As such, this is the major cause of overfitting in many data mining objects. Therefore, creating a huge problem during the research process results in more bias.
Data cleaning is one of the basic methods of preventing overfitting. When the data is cleaned, it is easy to eliminate the bias that may cause the overfitting of the applied statistical model. Despite that, the major causes are bias and some of the small errors committed, like omission. The CSV data is loaded to a software where the data omitted is well taken care of in the process. Thus, it becomes easy to assess the major causes of the bias in the process. Therefore, the researcher can collect all the required data and create a clean short of the new model for better overfitting through data cleaning.
Getting more training data is also another method of avoiding overfitting data salience. For instance, the more the data is trained, the more accurate the data model becomes. The researcher will need to employ more samples of data and fit them into the model. However, in the real world, the major issue becomes how to solve the restriction time. It becomes a big challenge to manage all the data set training at the same time. Therefore, with sufficient time it becomes easy to manage all the data set training and avoid overfitting.