Dataset Optimization: Enhancing Machine Learning Performance Through Data

To enhance machine learning performance, there are four key areas you must optimize. That’s the algorithms, compute, training practices, and data. Today, we focus on data.

If you ignore dataset optimization, there’s a high likelihood of building bias or unfair machine learning models. This occurs when you feed models imbalanced datasets.

Besides unfairness, poor dataset optimization contributes to wasted compute resources because you are working with messy data. Such data increases storage needs, training time, and cost.

Here’s how to optimize machine learning (ML) datasets to prevent these or more troubles that come with messy data.

Dataset Optimization Techniques to Enhance ML Performance

For this piece, we’ll focus on the most employed basic and advanced dataset optimization techniques. After obtaining datasets for machine learning, don’t skip data cleaning. The nature of a dataset and the data requirements of a model should inform you when it is relevant to apply the other techniques.

1. Data cleaning

Even if you obtain an off-the-shelf machine learning dataset, check for missing values, duplicate records, outliers, and incorrect formatting. Checking for these errors is what we refer to as data cleaning.

A dataset with numerous missing values can confuse a learning model. If you are working with a small dataset, filling the gaps with reasonable estimates is okay. For massive datasets, get a predictive model to help with generating estimates.

Duplicate records, on the other hand, lead to bias. This is because the machine learning model believes the duplicate entries have more importance. Use tools like Pandas or OpenRefine to get rid of duplicates.

Lastly, outliers are values that do not fit the expected range. For example, an employee income record of $2,000,000, yet incomes are supposed to range between $20,000 to $60,000.

Be careful when assessing outliers. Sometimes, outliers result from incorrect entries or formatting. And, not all outliers lack significance, impacting model performance negatively when removed.

2. Data balancing

Picture this: You have a model that’s supposed to detect a particular disease. Out of 1600 patient records in the training dataset, 1500 are for healthy patients while 100 represent the sick. What’s going to happen here?

Yes, the AI model will mostly predict patient records as “healthy.” The model would fail to achieve the set objective — identifying sick patients. So, how do we solve this case of imbalance in a dataset?

One, you can oversample the minority class (the 100 patient records). Use techniques like Synthetic Minority Oversampling Technique (SMOTE) to generate additional synthetic but realistic records of sick patients.

You can also reduce the number of “healthy patient” records to 100 to balance the classes. However, you risk disregarding potentially crucial data. That’s why many prefer a hybrid approach to solve this problem. You combine both undersampling and oversampling to retain useful information as much as you can.

Apart from adjusting class values, you can focus more on adjusting how the algorithm learns. Adjust the model to focus more on the minority class during learning.

3. Data augmentation

There are times you may desire to train a machine learning model only to realize that the available datasets are thin. Collecting new data may not be in the question because of tight resources or of how sensitive or specialized the problem is. Here’s where augmentation comes in!

Augmentation is the process of expanding or diversifying a dataset without collecting new data. You modify the values of the existing dataset to create unique examples.

If your dataset contains image data, you can flip, crop, rotate, change the brightness, or resize the images. For text data, adding the synonyms of words, shuffling words, or even randomly inserting or deleting text counts.

You can also modify video and audio data. Add background noise to the audio,shift the time, or stretch the time. The goal is to provide the machine learning model with variety, ensuring that it extracts relevant patterns from the data rather than memorizing.

Other than modifying parts of the limited dataset, you can also use advanced models to generate new examples. For example, LLMs (Large Language Models) can generate paraphrased sentences. And, you can employ simulators to create synthetic environments, especially when collecting data that would be dangerous or costly to collect in real life.

4. Feature selection or engineering

Speaking of machine learning datasets, a feature is an individual measurable variable or property of a dataset. Think of it as the column in a structured dataset.

For instance, if you have a housing prices dataset, features include, age of the house, square footage, number of bedrooms, and location.

Rather than giving a model the dataset as is, you can identify and keep only the most relevant variables. This is what we call feature selection.

Say the housing prices dataset includes a feature like, color of the front door. This feature has less impact on pricing. So, you get rid of it. The same goes for noisy and redundant data.

Apart from selecting the most relevant features, you can also transform existing features or create new ones to make a dataset more meaningful for a model. A good example is converting “date of birth” to “age” to enhance understanding.

Both feature selection and engineering make models faster and efficient because they only learn from the most meaningful features. The two also boost accuracy as the learning model focuses more on patterns that actually matter.

5. Normalization and standardization

Normalization and standardization are both about rescaling numerical features. While normalization is the process of ensuring feature values fall within a specific range, standardization is the process of transforming a dataset so that its standard deviation becomes 1 and the mean becomes 0.

The most preferred normalization range is 0 to 1, what computers understand. To normalize data, you subtract the minimum value of a feature and divide the output by the feature’s range (max - min). This way, no feature dominates simply because it is a greater numerical value. All values fall under one range, 0 to 1.

To standardize data, calculate the mean of the values of a feature. Then, calculate the feature’s standard deviation and subtract the mean from each value before dividing by the standard deviation. This ensures that the data follows a standard normal distribution.

Normalization is essential when working with algorithms that rely on distance calculations or clustering methods. And, standardization counts in algorithms like linear regression or logistic regression since they assume that data is centered and scaled.

Closing Words

Before feeding data into an AI model, optimize it through either of the seven discussed techniques. Dataset optimization lowers the likelihood of building a bias model or wasting resources and time.

As you optimize various datasets, remember to set up systems to efficiently and securely store and retrieve data. Without this, data could be exposed to cyberattacks, leaks, and unauthorized access. Also, if stored data gets tempered with, duplicated or corrupted, it can ruin model performance.