Training dataset: This is the main dataset used for training a machine learning model. It contains examples of input data (also called features or predictors) and their corresponding output (also called labels or targets). The model learns from these examples and tries to generalize to new, unseen examples. Typically, the training dataset is the largest of the datasets used in machine learning.
Validation dataset: This is a separate dataset used to evaluate the performance of a machine learning model during training. It is used to tune hyperparameters, which are settings that affect how the model is trained. By using a validation dataset, you can avoid overfitting, which occurs when the model performs well on the training data but poorly on new, unseen data.
Test dataset: This is a dataset used to evaluate the performance of a machine learning model after it has been trained and tuned. The test dataset should be completely independent of the training and validation datasets. Its purpose is to simulate how the model will perform on new, unseen data.
Unlabeled dataset: This is a dataset that contains input data but no corresponding output. The purpose of an unlabeled dataset is to discover hidden patterns or structures in the data. Unsupervised learning algorithms, such as clustering or dimensionality reduction, are often used on unlabeled datasets.
Transfer dataset: This is a dataset that is similar to the training dataset but comes from a different source or domain. Transfer learning techniques can be used to leverage knowledge from the transfer dataset to improve the performance of a machine-learning model on the target dataset.
Notes:
1. Training, test, and validation dataset should contain no duplicate data points.
2.
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