Overfitting in machine learning: Understanding the concept
Overfitting is a universal challenge in machine learning, where a model excessively learns from the training dataset to an extent that it negatively affects the model's effectiveness on new data. Essentially, overfitting arises when a model learns not just inherent patterns but also noise and random fluctuations in the training sample. Consequently, such a model, known as an overfitted model, fails to generalize to fresh data due to its limited ability.
Models which are nonparametric and nonlinear in nature are more susceptible to overfitting due to their high flexibility in learning a target function. To manage this, numerous nonparametric machine learning algorithms involve parameters or methodologies to restrict the amount of details the model learns from. For instance, Decision trees, a nonparametric machine learning approach, which offers immense adaptability but is also prone to overfitting. The problem here can be tackled by pruning the tree after the learning process - thereby eliminating some acquired information.
Identifying overfitting in machine learning can be a task as it's hard to predict how a model will perform on unseen data before actually testing it. A common approach to help identify this is by dividing the initial data into training and testing subsets. A model that outscores on the training subset compared to the testing subset potentially overfits the data. For instance, a discrepancy in accuracy - such as 95% on the training set versus 65% on the testing set - can be a strong indicator of overfitting. A starting strategy here can be to utilize a simple model as a benchmark. This approach ensures that any added complexity in future algorithms can be measured against a preset baseline.
While identifying overfitting is crucial, it alone doesn't resolve the issue. However, there are multiple methods on how to avoid overfitting in a model:
- Early stopping technique: This involves ceasing the training process before the model begins overfitting the training sample. Although this method is mainly utilized in deep learning, alternate methods like regularization have found use in traditional machine learning.
- Cross-validation: Cross-validation serves as an effective measure to prevent overfitting. In this process, multiple train-test splits are formed within your initial training data, which helps in fine-tuning your model.
Overfitting vs underfitting
Underfitting is just the opposite of overfitting, occurring when the model is too simple and hardened by limited features or excessive regularization. While overfitted models possess high variance, underfitted models show less volatility but lean more towards incorrect results.
In machine learning, both bias and variance are integral parts of prediction error. We generally witness a bias-variance tradeoff where we compromise on one to reduce the other. The struggle to balance between being overly simplistic (high bias) and being exceedingly complex (high variance) is inherent to statistics and machine learning, and it influences all supervised learning algorithms.
In conclusion, overfitting is a frequently encountered issue not just in machine learning, but also in data science. In applied machine learning, overfitting tends to be the predominant problem. Notably, the ultimate concern is not the performance of machine learning algorithms on training data but their efficacy on unseen data. In such circumstances, K-fold cross-validation, a highly popular resampling method, comes in handy as it enables training and testing of the model on different data subsets, thereby estimating the model's performance on new data.