In this post I am going to make a compilation of interview questions for data science role. A big part of them are questions that I faced during my interviews. I have also gathered questions from different websites and which I found interesting. So, lets get started.

### What do you know about bias-variance/bias-variance tradeoff?

In statistics and machine learning, the biasâ€“variance tradeoff (or dilemma) is the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training set [Wikipedia]:

- The
*bias*is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting). Bias are the simplifying assumptions made by a model to make the target function easier to learn. Examples of low-bias machine learning algorithms include: Decision Trees, k-Nearest Neighbors and Support Vector Machines. Examples of high-bias machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression [2]. - The
*variance*is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting). Variance is the amount that the estimate of the target function will change if different training data was used.**Low variance**suggests small changes to the estimate of the target function with changes to the training dataset.**High variance**suggests large changes to the estimate of the target function with changes to the training dataset. Generally, nonparametric machine learning algorithms that have a lot of flexibility have a high variance. For example, decision trees have a high variance, that is even higher if the trees are not pruned before use. Examples of low-variance machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression. Examples of high-variance machine learning algorithms include: Decision Trees, k-Nearest Neighbors and Support Vector Machines [2].

The bias-variance tradeoff is a central problem in supervised learning. Ideally, one wants to choose a model that both accurately capture the regularities in its training data, but also generalizes well to unseen data. Models with low bias are usually more complex (e.g. higher-order regression polynomials), enabling them to represent the training set more accurately. In the process, however, they may also represent a large noise component in the training set, making their predictions less accurate – despite their added complexity. In contrast, models with higher bias tend to be relatively simple (low-order or even linear regression polynomials) but may produce lower variance predictions when applied beyond the training set.

### How can you tell that your model is biased?

Learning curves can tell whether the model appear to suffer from high bias or high variance.