Training data and testing data are two crucial components in the development and evaluation of machine learning models. Here's how they differ
1. **Purpose**:
- **Training Data**: This dataset is used to train the machine learning model. During training, the model learns patterns and relationships in the data.
- **Testing Data**: This dataset is used to evaluate the performance of the trained model. It is not used during the training phase but is reserved for assessing how well the model generalizes to new, unseen data.
Training Data: This data subset is used to train a machine learning model. During training, the model learns patterns, relationships, and features from this data.
Testing Data: Testing data, also known as validation data or test set, is used to evaluate the performance of the trained model. It assesses how well the model generalizes to new, unseen data
Availability:
Training Data: Available data that is used for model development. It usually constitutes a larger portion of the dataset.
Testing Data: Separate data that is not used during the training phase. It's kept aside to assess the model's performance.
Purpose of Use:
Training Data: Used to teach the model the underlying patterns and relationships in the data.
Testing Data: Used to measure how well the model can make predictions or classifications on new, unseen data.
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Answer:
Training data and testing data are two crucial components in the development and evaluation of machine learning models. Here's how they differ
1. **Purpose**:
- **Training Data**: This dataset is used to train the machine learning model. During training, the model learns patterns and relationships in the data.
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Verified answer
Purpose:
Training Data: This data subset is used to train a machine learning model. During training, the model learns patterns, relationships, and features from this data.
Testing Data: Testing data, also known as validation data or test set, is used to evaluate the performance of the trained model. It assesses how well the model generalizes to new, unseen data
Availability:
Training Data: Available data that is used for model development. It usually constitutes a larger portion of the dataset.
Testing Data: Separate data that is not used during the training phase. It's kept aside to assess the model's performance.
Purpose of Use:
Training Data: Used to teach the model the underlying patterns and relationships in the data.
Testing Data: Used to measure how well the model can make predictions or classifications on new, unseen data.