Some Deep Learning and Neural Networks Keywords

What is Overfitting?

When the training data contains information about the regularities in the mapping from input to output, but it also contains sampling error

How to Prevent Overfitting?

• Approach 1: Get more data

• Approach 2: Use a model that has the right capacity

• Approach 3: Average many different models

What is Variance?

This is when you fit the model to another training set drawn from the same distribution over cases

What is a Training Set?

used for learning the parameters of the model

A training set is a collection of data used to train a model. This dataset contains input-output pairs where the inputs are the features or variables, and the outputs are the target values or labels that the model aims to predict. The training set is fundamental to the learning process because it allows the model to learn the patterns and relationships within the data.

Here’s a more detailed breakdown:

1. Purpose: The training set is used to teach the model by example. During training, the model iteratively processes the training data and adjusts its parameters to minimize errors in its predictions.

2. Composition: It typically consists of a large number of labeled examples. Each example includes:

   • Features (Inputs): These are the attributes or variables used to make predictions. For instance, in a dataset for house prices, features might include the size of the house, the number of bedrooms, the location, etc.

   • Labels (Outputs): These are the target values or outcomes the model is being trained to predict. In the house price example, the label would be the price of the house.

3. Usage in Training: The model uses the training set to learn. This process usually involves:

   • Forward Propagation: Calculating the model’s predictions based on current parameters.

   • Loss Calculation: Measuring the difference between the model’s predictions and the actual labels.

   • Backward Propagation: Adjusting the model’s parameters to reduce the prediction error using optimization algorithms like gradient descent.

4. Splitting Data: In practice, the available data is typically split into three sets:

   • Training Set: Used to train the model.

   • Validation Set: Used to tune model parameters and make decisions about model architecture to prevent overfitting.

   • Test Set: Used to evaluate the model’s performance on unseen data to assess its generalization capability.

5. Quality and Size: The quality and quantity of the training set are crucial. A large and diverse training set generally helps the model learn better and generalize well to new data. If the training set is too small or not representative of the real-world scenarios, the model might not perform well on new, unseen data.

What is a Validation Set?

A validation set in machine learning is a subset of data used to evaluate a model during training. Unlike the training set, which is used to fit the model, the validation set is used to fine-tune the model's hyperparameters and make decisions about its architecture. The goal of the validation set is to provide an unbiased evaluation of a model fit on the training dataset while tuning the model's parameters.

Here are key points about the validation set:

1. Purpose: The primary role of the validation set is to ensure that the model generalizes well to unseen data. It helps in assessing whether the model is overfitting (performing well on the training set but poorly on new data) or underfitting (performing poorly on both the training set and new data).

2. Hyperparameter Tuning: During the training process, various hyperparameters (e.g., learning rate, number of layers in a neural network, tree depth in decision trees) need to be optimized. The validation set provides feedback on how these hyperparameters should be adjusted.

3. Model Selection: In scenarios where multiple models are being evaluated, the validation set helps in selecting the best-performing model. For instance, comparing different algorithms, architectures, or feature sets.

4. Avoiding Overfitting: By evaluating the model on a separate set of data not used for training, the validation set helps in detecting overfitting. If a model performs significantly better on the training set compared to the validation set, it is likely overfitting.

5. Training Process: Typically, the data is split into three parts:

   • Training Set: Used to train the model.

   • Validation Set: Used to tune hyperparameters and make decisions about model changes.

   • Test Set: Used to evaluate the final model performance.

6. Cross-Validation: In some cases, especially when the dataset is small, k-fold cross-validation is used. In k-fold cross-validation, the data is split into k subsets, and the model is trained k times, each time using a different subset as the validation set and the remaining data as the training set. This ensures that every data point gets to be in the validation set exactly once, providing a more robust evaluation.

7. Size: The size of the validation set can vary, but a common practice is to allocate about 10-20% of the total data for validation. The exact proportion can depend on the overall size of the dataset and the specific needs of the model and problem.

8. Bias and Variance: The validation set helps in maintaining a balance between bias (error due to overly simplistic models) and variance (error due to overly complex models). By monitoring performance on the validation set, one can adjust the model to achieve the right complexity.

What is a Test Set?

A test set in machine learning is a subset of data used to evaluate the performance of a fully trained model. This dataset is not used during the training or validation phases and serves as a final check to estimate the model's generalization capability to unseen data.

Here are the key points about the test set:

1. Purpose: The primary role of the test set is to provide an unbiased assessment of how well the model performs on new, unseen data. It helps in determining the model's accuracy, robustness, and overall predictive performance after the model has been trained and validated.

2. Separation from Training and Validation Sets: The test set is distinct from both the training set and the validation set to ensure that the evaluation metrics reflect the model's ability to generalize rather than its ability to memorize the training data. The data used in the test set should not influence any aspect of model training or hyperparameter tuning.

3. Final Evaluation: After a model has been trained on the training set and fine-tuned using the validation set, the test set is used for the final evaluation. Performance metrics obtained from the test set provide a realistic estimate of the model's performance in a real-world application.

4. Metrics: Common evaluation metrics assessed on the test set include accuracy, precision, recall, F1 score, mean squared error (MSE), area under the curve (AUC), and others, depending on the type of problem (classification, regression, etc.).

5. Size and Selection: The size of the test set can vary but is typically around 10-20% of the total dataset. The test set should be representative of the problem domain to ensure that the performance metrics are accurate reflections of the model's generalization ability.

6. Avoiding Data Leakage: It is crucial to ensure that no data from the test set is used during the training or validation phases to prevent data leakage, which can lead to overly optimistic performance estimates.

7. Cross-Validation and Test Sets: Even when using techniques like k-fold cross-validation for model tuning and validation, a separate test set is often reserved for the final evaluation. This ensures that the performance metrics are not influenced by the cross-validation process.

8. Model Comparison: The test set is also used to compare different models or approaches. By evaluating multiple models on the same test set, one can determine which model performs best under consistent conditions.