Machine learning has been fundamental in developing apps that predict trends and movements. But what about things that are impossible to get right, like the weather?
That’s what researchers at the Ohio State University set out to build recently. Their model uses what they call ‘next-generation reservoir computing.’ The details are complicated, as expected of a machine learning model, but the gist is that it can learn from chaotic phenomena, such as sudden changes in the weather. Imagine not blaming the weatherman anymore for being wrong.
As promising as this technology seems, you have to ask yourself: ‘Is this one right for what I’m making?’ With four types and a gaggle of models to choose from, machine learning requires as much critical thinking. Consider the following tips and tricks for selecting the correct machine learning algorithm.
Know Your Data
In machine learning, data is life. How an algorithm works depends on the type of data it receives and processes. You can’t expect it to learn without feeding it copious amounts of data.
There’s an entire hierarchy of data types: quantitative vs. qualitative, discrete vs. continuous, etc. When creating your model, you’ll likely have to sort data between labeled and unlabeled. Their main difference lies with annotations, namely a lack thereof. An image of a cat with a ‘cat’ label is labeled data, whereas an image of a cat alone is unlabeled data.
Why is this important? Supervised learning involves labeled data, while unsupervised learning involves unlabeled data. Algorithms made with supervised learning, such as decision trees and neural networks, use either classification or regression, depending on the type of target variable.
In many scenarios, developing a model requires unsupervised learning. Without target variables, you can only employ either association or clustering techniques. You can look up more on these techniques on online resources like https://cnvrg.io/ and other sites. Also, note that clustering can also work in semi-supervised learning.
Accuracy Versus Speed
You can’t have your cake and eat it, too—that’s as much true in machine learning. Do you want a model that achieves the highest possible accuracy or generates results quickly? No matter how you make your algorithm, as far as accuracy and speed go, this step will involve compromise.
Experts are somewhat divided on this topic. Some say speed isn’t an issue, especially during training, and development should lean more toward accuracy. Others stress that accuracy won’t need as much focus, especially when deployed since users rarely have the patience to wait for an answer.
Your decision between prioritizing accuracy or speed will determine the most suitable algorithm. Regression-type algorithms (e.g., logistic and linear regression) are easy to develop and execute but have low accuracy. There are ways to improve its accuracy, but the additional work required might defeat the purpose of a quick execution.
On the other hand, decision trees and random forest are well-known for their high accuracy but need more time to work. If you prioritize accuracy, the least you can do is ensure the benefits far outweigh the time required. If the model generates a mediocre result, you’re probably better off developing high-turnaround algorithms.
Algorithms are versatile enough to be applicable in various industries. Take fraud detection for financial applications, for example. Random forest and k-nearest neighbors are among the most suitable algorithms trained via supervised learning. The former excels because it works with an assortment of variables, allowing it to take in massive datasets.
Among unsupervised learning algorithms, k-means clustering is a favorite. Data with more features than data points works with a variant of k-means clustering known as constrained k-means or self-organizing map.
Amidst their versatility, some algorithms are more suitable for specific applications than others. Sticking to tried-and-true algorithms for each application is a surefire way to choose the correct one. Below are several examples.
- Logistic regression is widely used in demographics, particularly in defining the effect of resource use on population growth. This is referred to as logistic population growth, and it’s a staple in studying the ecological aspects of such development.
- Linear regression finds extensive use in market forecasting. It’s efficient at isolating two instances running simultaneously (i.e., marketing campaigns on two different media). In doing so, businesses can gauge the success of each campaign and make adjustments.
- Principal component analysis is at the core of multiple facial recognition technologies. The eigenface, the simplest of these approaches, utilizes variance data derived from a collection of facial images. As a result, these apps can identify even the blurriest photos.
There’s no such thing as a one-size-fits-all machine learning algorithm. From predicting market movement to recognizing persons of interest, there’s an algorithm for that. It all boils down to these three key aspects:
- Knowing the kind of data the algorithm will learn from and process.
- Weighing the pros and cons of prioritizing accuracy or speed in a situation.
- Studying the industry or market in which the app will operate over its lifetime.
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