When it comes to machine learning algorithms, your predictions are only as good as your data. And if you aren’t using the right data, you aren’t setting your models up for success. This interesting topic came to us from KD Nuggets in their article, “The Difference Between Training and Testing Data in Machine Learning.”
When building a predictive model, the quality of the results depends on the data you use. This requires an understanding of the difference between training and testing data in machine learning.
Machine learning uses algorithms to learn from data in datasets. It finds patterns, develops understanding, makes decisions, and evaluates those decisions. Training data is typically larger than testing data. This is because you want to feed the model with as much data as possible for it to find and learn meaningful patterns. Once data from our datasets are fed to a machine learning algorithm, it learns patterns from the data and makes decisions.
The possibilities of machine learning and artificial intelligence (AI)-based prediction are endless. With the right dataset, you can build the needed model, start scoring, and get a bigger business output.
Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval, and explainable AI.
Melody K. Smith
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