Remember that training an AI model is like teaching a child how to do a specific task. The model will not perform well in real-life situations if you provide incorrect or low-quality data. Today, in this article, we’ll explore how to increase the quality of your data for your machine learning model to have good accuracy. Let’s find out.
Table of Contents
- Make sure the data collected is quality and diverse
- Deal with missing data
- Remove duplicate data points
- Remove outliers
- Delete or fix the data that do not fit in certain regular expressions
- Equalize the data and remove bias
- Make new features out of existing ones
- Improve your data with assessments of your model
1. Make Sure that the Data Collected is Quality and Diverse
When you want to get your data to high quality, optimize your data collection process. Before you obtain the data, it’s essential to know your model’s purpose and what kinds of data it will face in the real world. Your model depends on the data you collect, so getting good-quality and diverse data is crucial. Otherwise, the model might learn from the flaws and biases of your information, and the predictions from the model will be less accurate.
For example, if your model is to classify an image into multiple categories, it should be trained with multiple batches of data that include data from different categories. Moreover, some photos should also be modified, rotated, of low quality, or shot at different angles to tell the model that it still belongs to some category even though it may not be a usual image.
2. Deal With Missing Data
After checking that your data has been collected correctly, it’s time to clean it up to minimize the amount of bias learned by the model. First, we need to deal with missing data. It could induce bias into your database in many ways, so it must be processed before you train your model.
The simplest way of dealing with missing data is to delete them. Whenever a data point has a piece of missing data that belongs to one or more columns used for training, it is deleted from the database. While this gives the benefit of ease of processing and letting the model learn from the well-documented and well-collected data, it is not applicable when there are significant amounts of missing data on the database. If so, this approach will shrink your database, leaving your model with less to learn and more errors in predictions.
We can fill missing data points with specific values to get around this problem. This is called data imputation, and the value chosen is often the mean or median of the entire column to ensure minimal bias. However, no matter which way you go, bias can still be introduced into the data, as the values of some other variables may make it more likely for missing data to occur. Nevertheless, this is very useful if you want to keep most of your data in the database for the model to have more to learn.
3. Remove Duplicate Data Points
Sometimes, one data point may be recorded multiple times in the dataset. In that case, some of them must be removed until only one instance of every distinct data point exists. It is because the model may skew towards the properties of some particular data points if duplicate data is introduced. Again, this introduces bias into the system, which is always detrimental. Therefore, each data point must be treated fairly, and duplicates must be removed to make the model more accurate.
4. Remove Outliers
Outliers are data points that deviate from average values by a significant amount. It can significantly affect the mean value of the entire dataset. While a person can easily spot the difference between a “normal” piece of data and an outlier and take fewer references from the “unusual” data point, a machine learning model, which is in the process of being trained, might not be able to do so.
Therefore, removing data points where pieces of data are near the minimum or maximum value is beneficial as it teaches the model what “normal” data looks like. This produces better results as the model can learn from the information it is supposed to receive.
However, there could be rare cases where most data points considerably deviate from what the model is supposed to predict. This essentially occurs when a model needs to predict data that belongs to a small patch of information from a large dataset. In that case, you should make sure most values in the model are what it is supposed to predict. Otherwise, it might produce vastly incorrect results. For instance, if the predicted value is much greater than the actual value, you know that the model is significantly affected by outliers much larger than the genuine values of the predictions.
5. Delete or Fix Data that Does Not Fit in Regular Expressions
For a computer to parse a string and analyze it without using natural language processing (NLP), it often needs to fit in regular expressions. An example of a regular expression is seen in email addresses, where a username is followed by an “@” character and a domain name.
Consequently, you should ensure that all of your data fits in certain regular expressions and formats. This ensures that the model can process more substantial amounts of data and that fatal errors will not occur often. If alternate data types are being detected, use a format converter (e.g., the str() and int() functions in Python) to turn a data type into another. If alternate regular expressions are detected, ensure that they are parsed differently so that raw data output can still be generated, or simply drop the data point using the non-standard data types.
6. Equalize the Data and Remove Bias
Imagine that you want to identify whether a patient has a particular rare disease. If you randomly sample data from the population, what will happen? If your model tries to simplify things as much as possible, it will return false every time. In that case, it still has a great amount of accuracy because that disease is rare, but it will not achieve its purpose of looking for people with that disease.
What should we do? Well, we should equalize the data. That means we should include the same amount of data for people who have contracted this disease and those who haven’t. In that case, the model will learn that we should take every report seriously, and the model will achieve its purpose in the real world, even if its accuracy may have decreased. It is because the bias that people don’t contract this disease is being removed from the data, and, subsequently, the model.
7. Make New Features Out of Existing Ones
If you want to improve the model’s performance after data cleaning, consider feature engineering. This process makes new features out of existing ones. More features mean more things for the model to learn and can stress some of the crucial patterns in the dataset, which means better performance for the model.
There are many ways for feature engineering, including encoding categorical variables, splitting one feature into multiple features, or combining various features into one. However, the method of grouping them must be carefully considered and tested since different methods may result in different model performances.
8. Improve Your Data With Assessments of Your Model
Last but not least, it’s crucial to assess your model’s performance to prepare for unexpected adverse events that will affect your model’s real-world performance. This includes overfitting, which is very easy to overlook and is often only detectable through testing.
An effective way to see if your model is performing well is through a train-test split. This means splitting your dataset into the train and test categories, where the “train” data is used for training the model, and the “test” data is meant for assessing the model. After you have trained your model, you have to predict the values of the test dataset using your model and compare your results with the test data to see if it is performing well.
If the assessment of your model returns that the predictions are all correct and have no errors, it is almost impossible in real-life situations, and you are likely training your model with the test dataset. In that case, retrain your model with only the training dataset.
If your model performs poorly on the test dataset, the culprit is likely underfitting or overfitting. If you have only a small amount of data, underfitting will occur since the model cannot identify a pattern that can be used to predict values accurately. On the other hand, if you have trained your model with a large amount of data, but this undesirable result still occurs, your model is probably overfitting, and you should make your dataset more diverse and equalized so that the model generalizes across all scenarios it will encounter in the real world.
To conclude, we have mentioned 8 ways to increase your data quality so that your model will perform better. Hopefully, these tips will become helpful when you plan to use a machine learning model for whatever purpose you want. If you noticed any important missing points or any wrong or incomplete statements in this article, please leave them in the comments below so that this article can be improved.