The Data thinning phase refers to the initial data collection process. The goal here is to create an overview of the entire dataset and determine where things start to get repetitive from one sample to the next. Once we have identified these samples, we will then narrow down our search space by finding those samples that are unique (or at least not duplicated) in some way.
In today’s article we will discuss Data Thinning and Its advantages.
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What is Data Thinning?
Data thinning is the removal of data records that don’t have any meaning or significance. Imagine if we had a million customer accounts in our database, but only 50% of those customers had orders placed. If we kept 100 000 records, then we would be storing unnecessary data, thus wasting space. This is why we would delete the remaining 50 000 records. If we were doing data mining, we could find statistical trends and patterns among records that fall under certain categories. We could use this information to predict future outcomes and develop predictive models.
Data thinning can be done manually, or automatically using software programs. The manual method requires a person to read each record and decide whether it should be deleted or not. While the automatic approach uses algorithms to identify records that do not carry any value and deletes them.
Types of Data Thinning
There are two major types of data thinning, namely proactive data thinning and reactive data thinning. In proactive data thinning, the algorithm looks at historical data to determine what type of action needs to be taken. When implementing reactive data thinning, algorithms look at current data for action. Both methods can be used together to enhance efficiency.
Advantages Of Data Thinning
Thinning reduces your monthly data storage costs because less space is taken up by old data. Also, less time and effort is spent backing-up large amounts of data.
By storing data on fewer devices, you’re saving valuable hard drive space. By reducing the amount of data stored, you’ll save money on your monthly cloud service bill. If you have several small files stored on various hard drives, they may take up lots of space. Storing those smaller files on just one hard drive could reduce your monthly cost dramatically.
Reducing the amount of data stored means you spend less time retrieving and backing-up information. This saves not only your time, but that of any IT staff who need to access the data. Having fewer backups also minimizes downtime during maintenance.
Because you don’t store backup copies of critical information, you’re minimizing your risk of losing sensitive information. You’ll also have fewer opportunities for a hacker to gain unauthorized access to your account.
If something goes wrong with your server, you won’t lose everything if you’ve reduced the size of your data set. Less data means you’ll have fewer problems. You’ll spend less time troubleshooting issues and will be able to repair things faster.
You’ll experience faster load times and increased performance thanks to decreased file sizes.
Decreased Storage Costs
When you buy more memory sticks and hard drives, you’ll rack up additional storage fees. That’s where data thinning comes in. By using a program like Dropbox or Google Drive, you can share files across multiple computers. When you do, you’ll use less storage space.
Why is Data Thinning used?
Data thinning is a method used in computer science where data is pruned away from a database to make room for future storage. An example would be deleting files on a hard disk drive after they are no longer being accessed. Data thinning is useful for many reasons and especially well suited for databases because it allows them to store less information while maintaining the same amount of accessibility. By removing unnecessary data, companies can save money on space and bandwidth.
What does data thinning mean?
Data thinning means removing data points from your model while keeping the predictive power of the model intact. Data point removal is done by setting a threshold value based on your expected accuracy – if you want predictions to be accurate at 95% then you set the threshold to 5%. Setting a low threshold makes sure you don’t remove any data points but setting a high threshold will keep your model robust. You can go back later and add all your removed data points once you feel confident enough with your model’s performance.
Why do we need to perform data thinning?
The only way to make sense of sparse data (e.g. less than 10 observations) is to use a model that can handle it. By removing those poor performing data points you can increase the predictability of your model. One problem with using a single regression model is that the model will not know how to adjust for the missing values. If you have lots of bad data points, then you might get a lot of zeros. Using a linear regression model will give you a predicted value of zero, which will be wrong. A polynomial regression model may improve the situation but will still fail to capture the trends of the data.
How many data points should I remove? Is there a rule of thumb?
There isn’t an exact answer to this question. However, generally speaking, you’re removing about half of your original observations. In terms of percentage, this number goes down as the square root of the sample size (i.e., doubling the sample size halves the percentage). So if you expect your model to fit around 80%, then you’d ideally remove 40% of your data.
Are data thinning techniques applicable to non-linear models?
Yes. We’ve shown here the application of data thinning to a simple linear model. But data thinning is just one technique to deal with sparse data. Another popular method is called imputation. Imputation involves replacing missing data values with sensible estimates. For example, you could replace the missing values with their average or median value.
When should I apply data thinning?
Generally speaking, before running a model. You can run some diagnostics to assess if your data is suitable for modeling. Then you can start applying data thinning techniques to reduce the effect of sparsity.
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