Overfitting is a common problem in machine learning, manifested by poor generalization of the model on the test data. When does overfitting occur? High variance in model performance is an indicator of an overfitting problem. The training time of the model or its architectural complexity may cause the model to overfit. The result is that the model learns about noise or useless information in the dataset.
- The difference between overfitting and underfitting
Underfitting occurs when the data is highly biased and as a result the model does not work well on the training data.
Underfitting occurs when:
- Use dirty training data that contains noise or outliers
- Model has high bias.
- The scene is more complex, but the model is too simple.
Overfitting occurs when the model has high variance, i.e. the model performs well on the training data but inaccurately on the evaluation set.
Overfitting occurs when:
- The data used for training was not cleaned and contained junk data, causing the model to capture the noise in the training data.
- Model has high variance.
- The amount of training data is not enough, and the model is trained for several epochs on the limited training data.
- The model's architecture has several neural layers stacked on top of each other. Deep neural networks are complex, require a lot of time to train, and often lead to overfitting of the training set.
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How to detect overfitting
One of the main indicators of an overfitting model is its inability to generalize to the dataset. So the easiest way to detect model overfitting is to split the dataset.
Among them, K-fold cross-validation is one of the most commonly used techniques for detecting overfitting. K-fold cross-validation divides the data points into k equal-sized subsets, called "folds". One split subset serves as the test set, and the rest of the folds will train the model. The model is trained on finite samples to estimate the overall performance of the model when predicting on unused data. Each fold serves as a validation set. After all iterations, we average the scores to evaluate the overall model performance.
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10 Tips to Avoid Overfitting
1. Use more data for training
As the training data increases, the key features to be extracted become prominent. The model can identify relationships between input attributes and output variables. The premise of this method is that the data input to the model is clean, otherwise, the overfitting problem will be exacerbated.
2. Data Augmentation
Another way to train with more data is data augmentation, which makes the sample data look different each time the model processes it.
3. Add noise to the input data
Another option similar to data augmentation is to add noise to the input and output data. Adding noise to the input stabilizes the model without compromising data quality and privacy, while adding noise to the output makes the data more diverse. Noise addition should be done within a certain range so as not to make the data incorrect or too different.
4. Feature Selection
Each model has several parameters or features, depending on the number of layers, number of neurons, etc. The model can detect many redundant features or features that can be determined by other features, leading to unnecessary complexity. We are well aware that the more complex the model, the greater the chance of the model overfitting.
5. Cross Validation
The complete dataset is divided into parts, and in standard K-fold cross-validation, we need to divide the data into k folds. We then iteratively train the algorithm on k-1 folds while using the remaining folds as the test set. This approach allows us to tune the hyperparameters of a neural network or machine learning model to test it.
6. Simplify data
Model complexity is one of the main causes of overfitting. Data reduction methods are used to reduce the complexity of the model to make it simple enough not to overfit. The process includes pruning decision trees, reducing the number of parameters in neural networks, and more.
7. Regularization
Overfitting also occurs if the model is too complex, so the number of features can be reduced. Regularization methods like L1 can be helpful if you are not sure which features to remove from your model. Regularization applies a penalty to input parameters with larger coefficients, subsequently limiting the variance of the model.
8. Ensemble study
It is a machine learning technique that combines multiple base models to produce an optimal predictive model. Common ensemble methods include bagging and boosting, which prevent overfitting because ensemble models are aggregated from multiple models.
9. Early stop
The method is to pause the training of the model until noise or random fluctuations appear in the memory data. The model may stop training prematurely, resulting in underfitting. So it is best to reach the optimal time for model training.
10. Add Loss Layer
Probabilistically dropping nodes from a network is a simple and effective way to prevent overfitting. In regularization, some layer outputs are randomly ignored or discarded to reduce the complexity of the model.