A collection of technical interview questions for machine learning and computer vision engineering positions.
If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. [src]
Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost).
Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for by an optimization algorithm.
ML/DL models essentially learn a relationship between its given inputs(called training features) and objective outputs(called labels). Regardless of the quality of the learned relation(function), its performance on a test set(a collection of data different from the training input) is subject to investigation.
Most ML/DL models have trainable parameters which will be learned to build that input-output relationship. Based on the number of parameters each model has, they can be sorted into more flexible(more parameters) to less flexible(less parameters).
The problem of Underfitting arises when the flexibility of a model(its number of parameters) is not adequate to capture the underlying pattern in a training dataset. Overfitting, on the other hand, arises when the model is too flexible to the underlying pattern. In the later case it is said that the model has “memorized” the training data.
An example of underfitting is estimating a second order polynomial(quadratic function) with a first order polynomial(a simple line). Similarly, estimating a line with a 10th order polynomial would be an example of overfitting.