How to Use Data Set Programming in Machine Learning

The results achieved by advanced machine learning algorithms may seem mind-blowingly mysterious to outsiders, but careful data set programming makes them possible. They involve things like understanding how the finished algorithm would ideally work, sourcing appropriate information, and preparing it to remove errors. Here are some critical steps to take when creating a data set to program an effective machine learning algorithm. 

1. Take Time to Understand and Define the Problem or Question

People normally develop machine learning algorithms because they need to solve a problem or answer a pressing question. Consider an example where an e-commerce retailer wants to know which products will most likely prompt shoppers to rebuy an item. In that case, the machine algorithm would likely include data about consumers’ past purchases and any other notable buying trends. 

The people who engage in data set programming will not eventually use the machine learning algorithm. Industries ranging from medicine to education use artificial intelligence (AI) in numerous ways. Programmers and data scientists don’t necessarily need firsthand experience working in those fields to build excellent algorithms. However, they should ideally spend time speaking with the people who use it. 

That’s because machine learning problem definition is often an iterative process that gets refined as people provide more details. Informational interviews with end users can be extremely valuable for learning more about how people experience a problem or need to have machine learning answer a question for them. The more insights you get from them, the easier it will be to empathize with their position and create data sets that make the machine learning algorithm work as everyone expects.

After understanding users’ needs, you can start thinking about the different capabilities of machine learning algorithms and how you might apply them. 

2. Begin Collecting the Data

Succeeding with data set programming requires having enough information for the machine learning algorithm to use. Something to decide early in the process is how much you’ll rely on your company or client’s information versus that contained in publicly available data sets. 

Fortunately, you’ll find plenty of sources for the latter. The United States government also maintains a website full of open data sets to consider. 

Another consideration during this step is what kind of data is most useful. When developing an algorithm for a relatively broad industry, such as health care or transportation, ask yourself what kind of information is most relevant to your use of machine learning. It’ll be much easier to determine if you rely on learnings from the previous step that required you to speak to the people who will use or directly benefit from your finished algorithm. 

An algorithm’s ability to make correct predictions depends on its access to past outcomes in the training data. That means it needs tremendous amounts of information. A commonly cited statistic is that you need approximately 10 times as many training data examples as your model has degrees of freedom. 

However, these amounts can vary based on individual use cases. Conversely, it’s virtually impossible to suggest a minimum amount of information that will still allow your algorithm to perform well. Generally, if your training data includes pictures or videos, you’ll need a larger data set than for other types of information. 

 3. Clean the Data

This stage is not the most glamorous part of data set programming for machine learning, but most data scientists spend significant time on it. That’s because the thoroughness of your data cleansing will greatly affect how accurately the resultant algorithm works and whether it answers the questions you want and expect. 

Start by removing unwanted or duplicate observations within the data set. Duplications are especially important to get rid of because they could introduce bias and influence you to come to incorrect conclusions.

Next, look for formatting errors — especially those related to data categories. You might see that the title of every category you’re using has a capital letter except one. In such a case, you’d want to fix it to have the same structure as the rest. The main reason is categories with the same names but different capitalizations could be treated as separate instances, interfering with the accuracy. 

It’s also important to remove outliers from the data, provided a legitimate reason for doing so. Be careful not to act too hastily, though. You may see a huge number in your data set, assuming it’s incorrect. However, it’s best to do further investigation to confirm whether that’s the case. 

Finally, handling missing data properly is a vital step in cleaning it. However, that doesn’t mean making assumptions and using your best guess to input what’s absent. It also does not involve eliminating parts of the information lacking values. Instead, the best approach to this common problem is to label that aspect as “missing.” If it’s a number, flag it first as missing, then fill it in with a zero. 

4. Participate in Feature Engineering and Selection

The last main step in machine learning using data set programming is feature engineering and selection. They’re similar but distinctive overall. Feature engineering occurs when you add or create new variables for the machine learning model to improve its output. This is the primary work done by data scientists. 

For example, they might change the data set’s composition by decomposing variables into separate features or using probability distributions to transform elements. These changes help to enhance the model’s output.

Feature selection occurs when data scientists examine the model to see what’s most relevant and eliminate what’s unnecessary. That’s an essential step because it makes model overtraining less likely to happen.

How Will You Use Data Set Programming?

Data set programming is an important part of machine learning because it collectively helps the algorithms work to their full potential. The care data scientists and related professionals take when taking these steps will have far-reaching effects on whoever uses or otherwise interacts with them.