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    Training data is used to increase the accuracy of machine learning algorithms with examples of desired and undesired outcomes. As a rule of thumb, the more data the algorithm is fed during training, the more accurate the results will be that the algorithm returns.

    How is training data used?

    The use of training data—also known as data sets—is one of the most significant parts of machine learning. The machine learning algorithm is only as accurate as the data it is trained on. Therefore, The quality and quantity of the data that an algorithm is first given will ultimately determine the machine’s ability to make good predictions.

    In rule-based traditional algorithms, the machines follow a set of instructions for input and output. However, with machine learning algorithms, historical data is used to describe the possible key. The machine must be able to access its past data in the form of image recognition, sentence context, or structure to proceed. 

    There are two distinct types of data used in training an algorithm. The first is the labeled data, also referred to as annotated data. This is used in supervised learning, allowing the machines to identify and classify a  continuous cache of data based on special characteristics, properties, and contained objects unique to the data pool. This method albeit the best is extremely time-consuming. 

    Unlabeled data, on the other hand, is used in machines that do not require supervision. This model is trained to make predictions using only patterns and similarities.

    Training data’s characteristics

    When choosing or creating training data, it is important to ensure the data is comprehensive. That data should include real-life samples of the desired attributes to train the model. The data should also be uniform in quality from reliable sources to ensure the integrity of the historical data. These attributes should also be relevant to the algorithm and representative of what a neural network or artificial intelligence can recognize. 

    Because different machines have varying data requirements, it might take a bit longer to collect confident intelligence. It also is important to note that data is not limited to simple yes/no and alphanumeric values. Training data can include texts, images, audio, and videos. Thus, the complexity of the training data set will have a direct impact on the expected nuances of the models’ performance. 

    Training data’s challenges

    There are a few possible challenges that can occur when formulating data for training. For one, the level (or lack) of diversity (race, gender, location, to name just a few) in the data will have an impact on the learning the algorithm does before being deployed into a production setting. In addition, the people or “humans in the loop” who collect and collate the raw data can affect the data sets accuracy, and may unknowingly reflect biases of those who collect and load the data. 

    Another challenge in gathering data of this type could be in the processes used to collect and process that data. To minimize data contamination, the team’s training nd procedures should include clear business and communication protocols to protect data integrity Due to the importance of training data in a model, solid quality control checks and clear parameters are integral. Finally, the tools used in compiling the data also impact its value. 

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    Training, testing, and validation data

    Training data is similar to a textbook in that is it meant to demonstrate to the algorithm what data “should” and “shouldn’t” produce the expected outcome. Test data, on the other hand, is expected to evaluate the performance and accuracy of the model. Test data should be more specialized and more reflective of “real-world” data in order to evaluate the quality of the machine’s predictions. Because of this, a constant flow of labeled data must be accessible to the algorithm.

    Test data also differs from validation data which is implemented frequently to evaluate the algorithm’s output during the training phase. Validation sets should only contain data that is known to be good by the algorithm’s developers but unknown to the algorithm itself as the machine is not expected to learn from this data. In some cases, cross-validation is used to randomly sort the data into multiple training sets and one validation set.