How to deal with overlearning in machine learning

How to deal with overlearning in machine learning

Overfitting is a phenomenon in which a machine learning model overfits the training data, resulting in poor generalization performance for new data.

1. data collection and expansion:

• Collecting more training data will allow the model to learn patterns more accurately.
• Use data extension techniques to transform existing data to generate new data and increase the diversity of training data.” See also “small data learning, combining logic with machine learning, and local/population learning.

2. data normalization:

• Standardize or normalize data to unify feature scales and equalize the impact of different features.

3. model complexity adjustment:

• To adjust model complexity and reduce overlearning, reduce the number of layers in the model or limit the number of parameters in the model. This makes the model less likely to overfit the training data.

4. cross-validation:

• Cross-validation is used to evaluate the generalization performance of the model. Cross-validation allows one to monitor whether over-training is occurring and to select an appropriate model. See also “Statistical Hypothesis Testing and Machine Learning Techniques” for more information.

5. Early Stopping:

During the training process, training is stopped when the performance of the validation data stops improving. This prevents over-training from progressing.

6. dropout (Dropout):

In neural network models, overlearning can be reduced by adding a dropout layer that randomly disables some units.

7. regularization:

Regularization terms such as L1 regularization and L2 regularization are added to the model loss function to constrain the model weights and prevent overlearning. For details, please refer to “Overview, Examples and Implementation of Sparse Modeling“.

8. Feature Selection:

Appropriate selection of features to be used for training the model can reduce overlearning.

9. Ensemble learning:

Ensemble learning, which combines multiple models, can be used to improve generalization performance and reduce overlearning. For more information, see “Overview of Ensemble Learning, Algorithms, and Examples of Implementations.

Algorithms used to deal with overlearning

Various algorithms and techniques exist that can be used to deal with overlearning. They are described below.

1. Regularization:

• L1 Regularization (Lasso): used to constrain model weights and remove unwanted features.
• L2 Regularization (Ridge): helps to reduce model weights and suppress over-learning.
• Elastic Net: combines L1 regularization and L2 regularization to combine the benefits of both.

2. Dropout:

• Dropout is a method of randomly disabling some units (neurons) in a neural network to train different sub-networks. This reduces the complexity of the model and inhibits over-training.

3. Early Stopping:

• This is a method of stopping the training of a model when the loss of validation data begins to increase during training. This prevents over-training.

4. bagging (Bootstrap Aggregating):

• Aggregating is a method of training multiple models using multiple bootstrap samples (a randomly selected subset of training data) and averaging their predictions. This reduces model variance and over-training.

5. Domain Adaptation:

• Domain adaptation is a method to ensure that the model generalizes well to data in a new domain. This method uses the transfer learning and domain adaptation algorithms described in “Overview of Transfer Learning and Examples of Algorithms and Implementations“ to improve the generalization performance of the model.

6 Feature Selection:

• To reduce overlearning, features can be selected for input to the model. A feature selection algorithm is used to select the most important features.

To address over-learning, it is important to carefully tailor the model training process and choose the best method for the nature of the data and the requirements of the project.

Challenges and countermeasures for dealing with machine learning overlearning

The following describes issues related to dealing with over-learning.

1. collecting and organizing data of adequate quantity and quality:

Preventing overlearning requires a large amount of diverse data, and when data is scarce, models are prone to overfitting to training data. The challenge is to collect and organize the right amount and quality of data. Strategies to address these issues include the following

• • Collect a variety of data: Collect a variety of data, not just the same types of data. Data from different conditions, perspectives, and contexts will reduce overlearning.
  • Improving data quality: Data quality can be improved by removing noise and outliers and by processing missing values in the data.
  • Data normalization: data can be normalized to make the scales of different features uniform. Common normalization methods include z-score normalization and minimum-maximum scaling.
  • Data Extension: For image data, data extension techniques can be used to diversify the data set. Specifically, this would be the application of operations such as rotation, flipping, cropping, brightness modification, etc.
  • Data balancing: If there is a class imbalance problem, data undersampling or oversampling should be performed to balance the data.

2. optimize data preprocessing:

Data preprocessing has a significant impact on model performance. It is important to properly process the data, including normalization, handling of missing values, and categorical data encoding. The measures for these are described below.

• • Handling of missing values: Appropriately handle missing values in the data set. This can be done by removing samples with missing values, assigning alternative values, or using a missing value prediction model.
  • Outlier detection and treatment: Outliers have a negative impact on model performance. Outlier detection methods (e.g., IQR or Z-score methods) should be used to identify outliers and process them as needed.
  • Encoding of categorical data: Select a method for encoding categorical data into numerical data. One-hot encoding and label encoding will be common.
  • Feature scaling: scale the features uniformly. Common methods include z-score normalization and min-max scaling.
  • Feature Selection: reduce the complexity of the model by removing unnecessary features or using dimensionality reduction techniques (principal component analysis or feature selection algorithms).

3. reducing model complexity:

Complex models are easier to fit to training data, increasing the risk of overtraining. The complexity of the model must be appropriately adjusted, and the challenge becomes the selection of an appropriate model architecture and hyperparameters. Strategies to reduce model complexity are discussed below.

• • Model Simplification: Simplifying the model architecture is the first approach. Reducing the number of layers or units can reduce the complexity of the model, for example, by reducing the number of layers or the number of filters in a convolutional neural network (CNN).
  • Regularization: Regularization provides a way to constrain the weights of the model; by adding regularization terms such as L1 regularization (Lasso) and L2 regularization (Ridge) to the loss function, the model weights can be kept small, thereby preventing the model from over-fitting the training data.
  • Early Stopping: Monitoring the performance of the validation data during training and stopping the training when performance no longer improves. This allows training to be terminated before the model over-trains on the training data, thereby improving generalization performance.
  • Dropout: Dropout will be a method of randomly disabling some units in a neural network. This allows different partial networks to be trained and reduces over-training.
  • Hyperparameter Tuning: Tune the hyperparameters of the model (learning rate, batch size, number of epochs, etc.) to find the optimal settings. Hyperparameter tuning has a significant impact on preventing overlearning.
  • Cross-validation: Use cross-validation to evaluate model performance and ensure that over-learning is not occurring. It is important to evaluate performance across multiple folds to confirm average performance.
  • Ensemble learning: Use ensemble learning (e.g., bagging, boosting) to combine multiple models to reduce overlearning.
  • Feature Selection: Reduce model complexity by removing unnecessary features or using dimensionality reduction techniques to reduce the feature space.

Reference Information and Reference Books

For reference information, see “General Machine Learning and Data Analysis” “Small Data Learning, Combining Logic and Machine Learning, Local/Group Learning,” and “Machine Learning with Sparsity”

For Reference book “Advice for machine learning part 1: Overfitting and High error rate“

“Machine Learning Design Patterns“

“Machine Learning Solutions: Expert techniques to tackle complex machine learning problems using Python“

“Machine Learning with R“等がある。

Beginner to Intermediate Level

1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
   Author: Aurélien Géron

   • Very practical. Covers regularization (L1/L2), early stopping, dropout, and data augmentation.

   • Shows how to visualize learning curves to detect overfitting.

   • Ideal for practitioners using Python.

2. Python Machine Learning (3rd or 4th ed.)
   Author: Sebastian Raschka & Vahid Mirjalili

   • Excellent coverage of overfitting prevention in classical ML and deep learning.

   • Includes feature selection, cross-validation, and ensemble techniques.

Deep Learning Focus

3. Deep Learning
   Authors: Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   • The canonical deep learning reference.

   • Theoretical and practical treatment of overfitting, capacity control, dropout, early stopping, and regularization.

4. Neural Networks and Deep Learning
   Author: Michael Nielsen

   • Beginner-friendly and conceptual.

   • Has a very intuitive explanation of how overfitting arises and how regularization helps.

Theoretical and Statistical Learning

5. The Elements of Statistical Learning
   Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman

   • Comprehensive and rigorous.

   • Includes discussion on model complexity, regularization (ridge/lasso), and bias-variance tradeoff.

6. Understanding Machine Learning: From Theory to Algorithms
   Authors: Shai Shalev-Shwartz and Shai Ben-David

   • Covers generalization theory, VC dimension, regularization, and learning guarantees.

   • Strong focus on the mathematical foundations of overfitting.

Advanced / Research-Oriented

7. Statistical Learning Theory
   Author: Vladimir Vapnik

   • Foundational book that introduces the VC dimension and the theoretical basis of overfitting and generalization.

   • Suitable for researchers and theoreticians.

8. Machine Learning: A Probabilistic Perspective
   Author: Kevin P. Murphy

   • A thorough and Bayesian approach to ML, with detailed sections on overfitting, model selection, and priors.

   • Covers variational inference and regularization from a probabilistic point of view.