Glossary

Test-Train Split

A test-train split is a technique used in machine learning to evaluate the performance of a model. It involves dividing the data set into two separate sets: one for training the model and the other for testing its performance. The training set is used to teach the model by adjusting its parameters until it can accurately predict the desired output. The test set is then used to evaluate the model's performance by comparing its predicted output to the actual output.

The purpose of a test-train split is to prevent overfitting, which occurs when a model is too closely tailored to the training data and performs poorly on new, unseen data. By evaluating the model's performance on a separate test set, it can be determined if the model is overfitting or not. If the model performs well on the test set, it is likely to perform well on new data, and if it performs poorly, adjustments can be made to improve its accuracy.

When splitting the data into test and train sets, it is important to ensure that the two sets are representative of the entire data set. This can be done randomly or by using stratified sampling to ensure that both sets have similar proportions of the different classes or categories in the data.

In summary, test-train split is a crucial technique in machine learning for evaluating the performance of a model and preventing overfitting. By dividing the data into separate test and train sets and evaluating the model's performance on the test set, it can be determined if the model is accurately predicting the desired outputs or if adjustments need to be made to improve its accuracy.

A wide array of use-cases

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