Underfitting and Overfitting in Machine Learning

Underfitting and Overfitting in Machine Learning - Download as a PDF or view online for free
A statistical model or a machine learning algorithm is said to have underfitting when a model is too simple to capture data complexities. It represents the inability of the model to learn the training data effectively result in poor performance both on the training and testing data. In simple terms, an underfit model’s are inaccurate, especially when applied to new, unseen examples. It mainly happens when we uses very simple model with overly simplified assumptions. To address underfitting problem of the model, we need to use more complex models, with enhanced feature representation, and less regularization. A statistical model is said to be overfitted when the model does not make accurate predictions on testing data. When a model gets trained with so much data, it starts learning from the noise and inaccurate data entries in our data set. And when testing with test data results in High variance. Then the model does not categorize the data correctly, because of too many details and noise. The causes of overfitting are the non-parametric and non-linear methods because these types of machine learning algorithms have more freedom in building the model based on the dataset and therefore they can really build unrealistic models. A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees.

Illustration of underfitting and overfitting in simple regression

Overfitting & Underfitting

Underfitting, good or robust fit and overfitted statistics outline diagram - VectorMine

Overfitting and Underfitting in Machine Learning

Overfitting and Underfitting in Machine Learning + [Example]

How to Guide: Overcoming overfitting in your ML models - Predibase - Predibase

How to Address Overfitting and Underfitting in LLM Training?

Regularization in Machine Learning - Scaler Topics

The Complete Guide on Overfitting and Underfitting in Machine Learning

Over-fitting vs Under-fitting in Machine Learning - datajango

Model Complexity & Overfitting in Machine Learning - Analytics Yogi

Overfitting & Underfitting in Machine Learning - Analytics Yogi

Overfitting and Underfitting in Deep Learning