Return to Answer

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

After training, this part of the dataset is used to used to test how well the model generalizes to unseen data and estimate its performance.

Another possibility (going by your question), is the use of Cross-Validation. This is performed when the dataset is too small. In such a case, a random split is performed on the dataset resulting in k non-overlapping subsets. The test error is then estimated by taking the average test error across k trials. [Image Source]

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

After training, this part of the dataset is used to used to test how well the model generalizes to unseen data and estimate its performance.

Another possibility (going by your question), is the use of Cross-Validation. This is performed when the dataset is too small. In such a case, a random split is performed on the dataset resulting in k non-overlapping subsets. The test error is then estimated by taking the average test error across k trials. [Image Source]

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

This part of the data is used to used to test how well the model generalizes to unseen datasets and estimate its performance.

Another possibility (going by your question), is the use of Cross-Validation. This is performed when the dataset is too small. In such a case, a random split is performed on the dataset resulting in k non-overlapping subsets. The test error is then estimated by taking the average test error across k trials. [Image Source]

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

After training, this part of the dataset is used to used to test how well the model generalizes to unseen data and estimate its performance.

Another possibility (going by your question), is the use of Cross-Validation. This is performed when the dataset is too small. In such a case, a random split is performed on the dataset resulting in k non-overlapping subsets. The test error is then estimated by taking the average test error across k trials. [Image Source]

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

This part of the data is used to used to test how well the model generalizes to unseen datasets and estimate its performance.

For any Machine Learning model, the available data is usually split into three sets:

Training Set:

The part of data used to train the model and learn the parameters of the network.

The data that remains after allocation of the Training Dataset, is split into the Validation and Test sets.

Validation Set:

This sample of data is used to provide an unbiased evaluation of a model fit on the training dataset. This helps in tuning model hyperparameters to improve the model performance. Eg: Changing the number of clusters ($k$) in a K-Means algorithm, or the pooling layers in a CNN.

Test Set:

This part of the data is used to used to test how well the model generalizes to unseen datasets and estimate its performance.

Another possibility (going by your question), is the use of Cross-Validation. This is performed when the dataset is too small. In such a case, a random split is performed on the dataset resulting in k non-overlapping subsets. The test error is then estimated by taking the average test error across k trials. [Image Source]