Machine learning models rely on data. A model is only as good as the data used to train it. Therefore, high-quality machine learning datasets are essential in the early stages.
An algorithm can only identify features and discover patterns accurately with good training data. So, it’s safe to say that training data is the most crucial part of machine learning and AI. Keep reading to learn about training data and its role in machine learning.
Training data is the dataset that data scientists use to train machine learning models. The model obtains and perfects its rules from training data. Therefore, the quality of AI datasets affects how well a machine learning model will develop.
Training data is also referred to as a learning set, training set, and training dataset. Human beings learn better from real examples. Similarly, machines need datasets to learn from. Machine learning models use training data to carry out tasks correctly.
Think of it like this: training data creates a machine learning model. Through the data, a model will know how to identify an output. A machine learning model usually evaluates training data over and over again.
It does that to better understand the data’s qualities and thus become more efficient. The two types of training data are labeled and unlabeled data.
Labeled data is data that has meaningful tags. These tags provide important information. Labeled data is also referred to as annotated data. An example of labeled data is a photo with the apple tag or an email marked as spam.
AI datasets that are usually labeled are useful for supervised learning. Supervised learning is where a human being tags data to inform a model about what exactly it must locate. Labeled machine learning resources and datasets make it easier for models to complete tasks.
Take, for example, a picture of fruits tagged as oranges, apples, and grapes. A machine learning model can use that labeled data to learn the qualities of each fruit. Then, it will use that information to group other images.
Unlabeled data is data not tagged with any identifying information. It is essential for unsupervised machine learning. Unsupervised learning is where human beings provide a model with raw unlabeled data and then task it with identifying trends in the data.
Take the previous example of a picture of fruits labeled as oranges, apples, and grapes. With unlabeled data, the picture will have no tags. The model will need to analyze the image by studying features like shape and color. After evaluating the images, the model can properly classify new data.
Data scientists must use high-quality training data in machine learning models. Doing so lets the models find the right answers for a given task. Besides the training data’s quality, the quantity also affects the model’s success.
Training data gives models the missing context they need to understand things. For example, a data scientist wants a model to tell the difference between an apple and a banana. They will provide the model with multiple quality examples of each. Such machine learning datasets will help the model learn the distinguishing features of the fruits.
Over time, the machine learning model can tell the difference. The quality of training data determines how successful a machine learning algorithm is.
Almost every person has heard the adage, ‘garbage in, garbage out.’ The adage holds a lot of truth regarding training data and machine learning models. For a model to perform well, data scientists must feed it with quality machine learning datasets.