Validation and Testing
Validation and testing plays important role in evaluating the performance of a trained model so that the model can be used for the generalized data. For Validation the Model, two important terms are used which are called Overfitting and Underfitting.
Overfitting
Overfittng is the condition in which the model performs well on the training data but poorly on unseen data (validation or test data). Also, the model has excessively high variance. It means that it is overly sensitive to small changes in the training data. This condition arises due to over complexity of the model. To prevent this, we can use the techniques such as regularization (e.g., L1 or L2 regularization), dropout, early stopping, or using more training data.
Underfitting
This is the condition when the model performs poorly on both the training data and unseen data. The model has excessively high bias, which means that it is unable to capture the underlying patterns in the data. This condition arises when the model is too simple or less training data is provided. Thus, adding more layers or neurons, providing more relevant features, or training the model for more epochs can prevent Underfitting.
Model Evaluation
This method involves testing of the performance of a trained model on unseen data. This helps us to understand how well our Moedel perform for the unseen instances. Model Evaluation includes the testing the accuracy, precision, recall, F1 score for classification tasks, or mean squared error (MSE) for regression tasks. The main goal is to select the best-performing model for the unseen data.
Start learning PyTorch for Beginners
Machine Learning helps us to extract meaningful insights from the data. But now, it is capable of mimicking the human brain. This is done using neural networks, which contain the various interconnected layers of nodes containing the data. This data is passed to forward layers. Subsequently, the model learns from the data and predicts output for the new data.
PyTorch helps us to create and train these neural networks that act like our brains and learn from the data.
Table of Content
- What is Pytorch?
- Why use PyTorch?
- How to install Pytorch ?
- PyTorch Basics
- Autograd: Automatic Differentiation in PyTorch
- Neural Networks in PyTorch
- Working with Data in PyTorch
- Intermediate Topics in PyTorch
- Validation and Testing
- Frequently Asked Questions
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