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How to Prepare Your Dataset for Machine Learning? A Basic Technique

If you use a biased dataset preparation for machine learning (ML), you will end up with an unethical or unfair ML model. Use a dataset with irrelevant or noisy features, and you end up with a time and computational resource-hungry ML model. These are some of the hiccups associated with building or sourcing a dataset and directly feeding it to a select model. This approach doesn’t work! So, what works?

After building or sourcing a dataset for machine learning, you must prepare it to suit the data requirements of a specific model. This is a critical part of ensuring a model serves its purpose effectively.

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To avoid the aforementioned hiccups and more, here is a basic data preparation technique that makes your data better when working with just about any ML model.

Dataset Preparation for Machine Learning

Bearing in mind that machine learning models learn through different approaches, we’ve boiled this technique down to the basic concepts. So, whether your model learns through supervised or unsupervised training, this technique should guide you through the most crucial steps of dataset preparation.

Remember, the primary goal is to have a dataset that is relevant, consistent, clean, and structured in a way that favors your select ML model. Delve in!

Examine and understand the Dataset

Before creating, outsourcing, or buying datasets for machine learning, you must clearly define goals and objectives to drive the process.

Now, you need to go back to the documentation defining the business context in which your dataset was generated and audit it. Reference the problem statement and the defined data requirements of the select machine learning model. Using this information, proceed to review or examine your dataset’s schema.

A dataset’s schema is the blueprint of a specific dataset, describing the organization of data, features, data types, relationships and constraints of a dataset.

Going through your dataset’s schema helps ascertain its alignment with the objective in mind. This is because you get to assess the relevance of the features, data types, and data relationships.

Moreover, you become aware of the limitations or rules put in place to ensure the data within the dataset fulfills its purpose in machine learning.

Clean the Dataset and Handle Missing Data

After referencing the dataset’s purpose and schema, proceed to identify and correct errors within the dataset.

Look for instances of duplicate data, missing data, inconsistencies, and other inaccuracies capable of negatively impacting the model’s performance.

For instance, if your dataset contains different variants of the same entry like “United States” and “USA,” standardize the entry to improve consistency. This reduces the chances of confusing an ML model, enabling it to accurately recognize patterns within the data.

Duplicate data entries not only distort the accuracy and reliability of a model, but they also lead to bias. However, before you get rid of duplicate values, go through the dataset schema. Perhaps there are rules defining why you should leave some duplicates intact.

As for missing values, you may remove or impute them. It is practical to eliminate missing values when they are minimal and don’t have a significant impact on achieving the set objectives. Else, impute the missing values to find estimates. Some imputation techniques you may use include mean, median, and mode.

Transform the Dataset

Once you have an error-free dataset, transform or change the data to make it easier for the algorithm to understand. By default, an ML model requires data standardized, formatted, or scaled in a particular manner to optimize learning and real-world problem solving. Why?

The data in your dataset may be too small, too big, or simply presented in a way that an ML algorithm does not understand.

Let’s say your dataset contains financial data with income values ranging from 10 to 1000, and age values ranging from 10 to 100. If you were to directly feed this data into a machine learning model, it is likely to prioritize the income values for decision making or prediction.

To solve this problem, we normalize the ranges to lie in a range of {0,1}, optimizing the algorithm’s decision-making or prediction process. Other solutions include using alternative data transformation tactics like Box-Cox, square root, and logarithmic transformation.

Deal with Imbalanced Data and Consider Creating New Features

As highlighted, using a biased dataset results in an unethical or biased model. A biased dataset is one that contains one class of data that significantly outweighs other classes. Feeding such data to a classification ML model makes it favor the majority class in real-world application. The solution? Handle data imbalance from the start.

Some techniques you may use to address data imbalance include resampling, working with ML algorithms that account for imbalance, or change performance metrics. For instance, you may undersample the majority class and oversample the minority class to resolve data imbalance.

If you still end up with an imbalanced dataset even after using either technique to correct the imbalance, engineer or create new features. Feature engineering involves converting raw into features that better align with your ML model’s needs.

For instance, if you have separate columns of “date” and “month,” you may convert them into a single feature, “date.” Overall, well-engineered features should enhance your model’s performance.

Split the Dataset and Simplify the Dataset by Reducing its Dimensions

So far, you’ve assessed the relevance of your dataset, cleaned, transformed, and balanced it. Perhaps, you’ve engineered features to support the purpose of a select ML model.

At this point, you can feed the data to the model. However, to evaluate the performance of the model after it learns, split the dataset into a training, testing, and validation subset.

Finally, if you aim to use the ML model for a niched solution, simplify the dataset by reducing dimensions. Find the most relevant features to the niched solution and eliminate the rest. Dimensionality reduction lowers the likelihood of computational and overfitting challenges.

Closing Words

Yes, machine learning can’t do without data. However, you can’t just create or source a dataset and directly feed it to a machine learning model.

The dataset in question must be pre-processed or prepared specifically for the select machine learning model. Essentially, an unprocessed dataset undermines the performance of the select ML model.

Fortunately, with the help of this guide, you can now avoid all the issues that come with using unprocessed data. Remember, the primary goal of preparing data for machine learning is to have a clean, consistent, and relevant dataset that aligns with the data requirements of the model.