Question

Which of the following are true about bias and variance of overfitted and underfitted models?
Options
Overlitted models have low bias
Overfitted models have low vanance
Underfitted models have low bias
Underfitted models have high bias
Clear Response​

Answer 1

Answer:

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Overfitting, underfitting, and the bias-variance tradeoff

Learn these foundational machine learning concepts with a worked example in Python, numpy, and scikit-learn

Steve Klosterman

Steve Klosterman

May 19, 2019 · 12 min read

Poorly fitting suits

Image by The Strategy Guy

Overfitting, underfitting, and the bias-variance tradeoff are foundational concepts in machine learning. A model is overfit if performance on the training data, used to fit the model, is substantially better than performance on a test set, held out from the model training process. For example, the prediction error of the training data may be noticeably smaller than that of the testing data. Comparing model performance metrics between these two data sets is one of the main reasons that data are split for training and testing. This way, the model’s capability for predictions with new, unseen data can be assessed.

When a model overfits the training data, it is said to have high variance. One way to think about this is that whatever variability exists in the training data, the model has “learned” this very well. In fact, too well. A model with high variance is likely to have learned the noise in the training set. Noise consists of the random fluctuations, or offsets from true values, in the features (independent variables) and response (dependent variable) of the data. Noise can obscure the true relationship between features and the response variable. Virtually all real-world data are noisy.

If there is random noise in the training set, then there is probably also random noise in the testing set. However, the specific values of the random fluctuations will be different than those of the training set, because after all, the noise is random. The model cannot anticipate the fluctuations in the new, unseen data of the testing set. This why testing performance of an overfit model is lower than training performance.

Overfitting is more likely in the following circumstances:

There are a large number of features available, relative to the number of samples (observations). The more features there are, the greater the chance of discovering a spurious relationship between the features and the response.

A complex model is used, such as deep decision trees, or neural networks. Models like these effectively engineer their own features, and have an opportunity develop more complex hypotheses about the relationship between features and the response, making overfitting more likely.

At the opposite end of the spectrum, if a model is not fitting the training data very well, this is known as underfitting, and the model is said to have high bias. In this case, the model may not be complex enough, in terms of the features or the type of model being used.