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What is Overfitting and Underfitting with example?

Author

Andrew Vasquez

Updated on February 17, 2026

What is Overfitting and Underfitting with example?

An example of underfitting. The model function does not have enough complexity (parameters) to fit the true function correctly. If we have overfitted, this means that we have too many parameters to be justified by the actual underlying data and therefore build an overly complex model.

Furthermore, what is Overfitting and Underfitting?

Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data.

Likewise, what is Underfitting and Overfitting in machine learning and how do you deal with it? For the uninitiated, in data science, overfitting simply means that the learning model is far too dependent on training data while underfitting means that the model has a poor relationship with the training data. Ideally, both of these should not exist in models, but they usually are hard to eliminate.

Moreover, what is Overfitting explained real life example?

If our model does much better on the training set than on the test set, then we're likely overfitting. For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.

How do I know if my model is Overfitting or Underfitting?

If "Accuracy" (measured against the training set) is very good and "Validation Accuracy" (measured against a validation set) is not as good, then your model is overfitting. Underfitting is the opposite counterpart of overfitting wherein your model exhibits high bias.

How do I fix Overfitting?

Handling overfitting
  1. Reduce the network's capacity by removing layers or reducing the number of elements in the hidden layers.
  2. Apply regularization , which comes down to adding a cost to the loss function for large weights.
  3. Use Dropout layers, which will randomly remove certain features by setting them to zero.

What is Overfitting problem?

Overfitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. Thus, attempting to make the model conform too closely to slightly inaccurate data can infect the model with substantial errors and reduce its predictive power.

How do I stop Overfitting and Underfitting?

Using a more complex model, for instance by switching from a linear to a non-linear model or by adding hidden layers to your neural network, will very often help solve underfitting. The algorithms you use include by default regularization parameters meant to prevent overfitting.

How do I know if Python is Overfitting?

You check for hints of overfitting by using a training set and a test set (or a training, validation and test set). As others have mentioned, you can either split the data into training and test sets, or use cross-fold validation to get a more accurate assessment of your classifier's performance.

What does Underfitting look like?

Underfitting is the case where the model has “ not learned enough” from the training data, resulting in low generalization and unreliable predictions. In high bias, the model might not have enough flexibility in terms of line fitting, resulting in a simplistic line that does not generalize well.

Is Overfitting always bad?

The answer is a resounding yes, every time. The reason being that overfitting is the name we use to refer to a situation where your model did very well on the training data but when you showed it the dataset that really matter(i.e the test data or put it into production), it performed very bad.

What is the best algorithm for prediction?

Top Machine Learning Algorithms You Should Know
  • Linear Regression.
  • Logistic Regression.
  • Linear Discriminant Analysis.
  • Classification and Regression Trees.
  • Naive Bayes.
  • K-Nearest Neighbors (KNN)
  • Learning Vector Quantization (LVQ)
  • Support Vector Machines (SVM)

How does Regularisation prevent Overfitting?

That's the set of parameters. In short, Regularization in machine learning is the process of regularizing the parameters that constrain, regularizes, or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, avoiding the risk of Overfitting.

What is Overfitting neural network?

One of the most common problems that I encountered while training deep neural networks is overfitting. Overfitting occurs when a model tries to predict a trend in data that is too noisy. The goal of a machine learning model is to generalize well from the training data to any data from the problem domain.

How do I reduce Underfitting?

Techniques to reduce underfitting :
  1. Increase model complexity.
  2. Increase number of features, performing feature engineering.
  3. Remove noise from the data.
  4. Increase the number of epochs or increase the duration of training to get better results.

How do I fix Underfitting neural network?

According to Andrew Ng, the best methods of dealing with an underfitting model is trying a bigger neural network (adding new layers or increasing the number of neurons in existing layers) or training the model a little bit longer.

What causes Underfitting?

Underfitting occurs when a model is too simple — informed by too few features or regularized too much — which makes it inflexible in learning from the dataset. Simple learners tend to have less variance in their predictions but more bias towards wrong outcomes.

What are the three types of machine learning?

Broadly speaking, Machine Learning algorithms are of three types- Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

How do you test Overfitting?

Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.

How do you calculate Overfitting?

To estimate the amount of overfit simply evaluate your metrics of interest on the test set as a last step and compare it to your performance on the training set. You mention ROC but in my opinion you should also look at other metrics such as for example brier score or a calibration plot to ensure model performance.

Why is Overfitting called high variance?

Variance is a metric used to evaluate the ability of the trained model to generalize to some test dataset. Models with low bias (which can learn from the training data well) often have high variance (and therefore an inability to generalize to new data), and this phenomenon is referred to as “overfitting”.

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What is Overfitting and Underfitting with example?