Original article was published on Artificial Intelligence on Medium
Underfitting & Overfitting — The Thwarts of Machine Learning Models’Accuracy
The Data Scientists remain spellbound and never bother to think about time spent when the Machine Learning model’s accuracy becomes apparent. More important, though, is the fact that Data Scientists assure that the model’s accuracy should never suffer from the transgressions of overfitting and Underfitting. It is essential to make a trade-off between getting a good accurate model and a balance to not develop a complicated model. The key to success is in reconciling what ‘s possible and what ‘s practical to achieve the best accuracy at minimum complication because it leads to model Generalization.
The goal of the Machine learning algorithm is to make accurate predictions for new, never-before-seen data. Supervised learning often requires human effort to build the training set, but afterward automates and often speeds up an otherwise laborious or infeasible task. And, In Unsupervised learning, the learning algorithm is just shown the input data and asked to extract knowledge from this data. So, building an accurate model is always a tedious job because it depends on other facts, also like training data, test data, pre-processing steps, multicollinearity, Regularization, or suitable parameters for the algorithms (e.g., Lasso Regularization, Ridge Regression, etc.).
In principle, Data Scientist builds a model on the training data, and that should be able to make an accurate prediction on new, unseen data that has the same characteristics as the training dataset. It became a matter of blissful if a model makes accurate predictions on unseen data. In the Data Science filed, it is applauded and said that mode can Generalize from the training set to the test set.
Everyone wants to build a model that can generalize accurately as possible.
The algorithm expects multiple variables or features for the training for better prediction. Let’s take an example of a model that takes a very simple feature like “Everybody who owns a house buys a car.”
Here, the model has limited variables, so the model is not able to capture all the aspects of the variability in the data. The model will do badly even on the training set. Hence, choosing too simple a model is called underfitting.
Here, the model shows low variance but high bias.
Building a model that is too complex for the amount of information that is provided to the model is called overfitting.
Overfitting occurs when a model is fit too closely to the particularities of the training set and obtain a model that works well on the training set but is not able to generalize to new data. The overfitted model works very well on the training dataset, but it doesn’t work accurately on the test dataset.
A statistical model is said to be overfitted when we train it with a lot of data. When a model gets trained with so much data, it starts learning from the noise and inaccurate data entire in our data set.
Overfitting, Generalization, and Underfitting together
Relationship of Model Complexity to Dataset Size
It’s important to note that model complexity is intimately tied to the variation of inputs contained in the training dataset. Usually, collecting more data points will yield more variety, so larger datasets allow building more complex models. However, simply duplicating the same data points or collecting very similar data will not help.
Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. It is crucial to baseline the model by evaluating the performance; otherwise, overfitting or Underfitting may occur. Sometimes model may capture noise from data, and then this can make the model overfitted.
Overfitting and Underfitting are a curse for the prediction. They lead to poor predictions on the new dataset.