Data Science Interview Questions
Explain what precision and recall are. How do they relate to the ROC curve?
Calculating precision and recall is actually quite easy. Imagine there are 100 positive cases among 10,000 cases. You want to predict which ones are positive, and you pick 200 to have a better chance of catching many of the 100 positive cases. You record the IDs of your predictions, and when you get the actual results you sum up how many times you were right or wrong. There are four ways of being right or wrong: TN / True Negative: case was negative and predicted negative TP / True Positive: case was positive and predicted positive FN / False Negative: case was positive but predicted negative FP / False Positive: case was negative but predicted positive Now, your boss asks you three questions: What percent of your predictions were correct? You answer: the "accuracy" was (9,760+60) out of 10,000 = 98.2% What percent of the positive cases did you catch? You answer: the "recall" was 60 out of 100 = 60% What percent of positive predictions were correct? You answer: the "precision" was 60 out of 200 = 30% ROC curve represents a relation between sensitivity (RECALL) and specificity(NOT PRECISION) and is commonly used to measure the performance of binary classifiers. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more representative picture of performance.
Explain what resampling methods are and why they are useful. Also explain their limitations.
Classical statistical parametric tests compare observed statistics to theoretical sampling distributions. Resampling a data-driven, not theory-driven methodology which is based upon repeated sampling within the same sample. Resampling refers to methods for doing one of these Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation)
Is it better to have too many false positives, or too many false negatives? Explain.
It depends on the question as well as on the domain for which we are trying to solve the question. In medical testing, false negatives may provide a falsely reassuring message to patients and physicians that disease is absent, when it is actually present. This sometimes leads to inappropriate or inadequate treatment of both the patient and their disease. So, it is desired to have too many false positive. For spam filtering, a false positive occurs when spam filtering or spam blocking techniques wrongly classify a legitimate email message as spam and, as a result, interferes with its delivery. While most anti-spam tactics can block or filter a high percentage of unwanted emails, doing so without creating significant false-positive results is a much more demanding task. So, we prefer too many false negatives over many false positives.
How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
Often it is observed that in the pursuit of rapid innovation (aka "quick fame"), the principles of scientific methodology are violated leading to misleading innovations, i.e. appealing insights that are confirmed without rigorous validation. One such scenario is the case that given the task of improving an algorithm to yield better results, you might come with several ideas with potential for improvement. An obvious human urge is to announce these ideas ASAP and ask for their implementation. When asked for supporting data, often limited results are shared, which are very likely to be impacted by selection bias (known or unknown) or a misleading global minima (due to lack of appropriate variety in test data). Data scientists do not let their human emotions overrun their logical reasoning. While the exact approach to prove that one improvement you've brought to an algorithm is really an improvement over not doing anything would depend on the actual case at hand, there are a few common guidelines: Ensure that there is no selection bias in test data used for performance comparison Ensure that the test data has sufficient variety in order to be symbolic of real-life data (helps avoid overfitting) Ensure that "controlled experiment" principles are followed i.e. while comparing performance, the test environment (hardware, etc.) must be exactly the same while running original algorithm and new algorithm Ensure that the results are repeatable with near similar results Examine whether the results reflect local maxima/minima or global maxima/minima One common way to achieve the above guidelines is through A/B testing, where both the versions of algorithm are kept running on similar environment for a considerably long time and real-life input data is randomly split between the two. This approach is particularly common in Web Analytics.
How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
Proposed methods for model validation: If the values predicted by the model are far outside of the response variable range, this would immediately indicate poor estimation or model inaccuracy. If the values seem to be reasonable, examine the parameters; any of the following would indicate poor estimation or multi-collinearity: opposite signs of expectations, unusually large or small values, or observed inconsistency when the model is fed new data. Use the model for prediction by feeding it new data, and use the coefficient of determination (R squared) as a model validity measure. Use data splitting to form a separate dataset for estimating model parameters, and another for validating predictions. Use jackknife resampling if the dataset contains a small number of instances, and measure validity with R squared and mean squared error (MSE).
Explain what regularization is and why it is useful
Regularization is the process of adding a tuning parameter to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector. This constant is often either the L1 (Lasso) or L2 (ridge), but can in actuality can be any norm. The model predictions should then minimize the mean of the loss function calculated on the regularized training set.
What is root cause analysis?
Root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. A factor is considered a root cause if removal thereof from the problem-fault-sequence prevents the final undesirable event from recurring; whereas a causal factor is one that affects an event's outcome, but is not a root cause. Root cause analysis was initially developed to analyze industrial accidents, but is now widely used in other areas, such as healthcare, project management, or software testing. Here is a useful Root Cause Analysis Toolkit from the state of Minnesota. Essentially, you can find the root cause of a problem and show the relationship of causes by repeatedly asking the question, "Why?", until you find the root of the problem. This technique is commonly called "5 Whys", although is can be involve more or less than 5 questions.
What is selection bias, why is it important and how can you avoid it?
Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample. For example, if a given sample of 100 test cases was made up of a 60/20/15/5 split of 4 classes which actually occurred in relatively equal numbers in the population, then a given model may make the false assumption that probability could be the determining predictive factor. Avoiding non-random samples is the best way to deal with bias; however, when this is impractical, techniques such as resampling, boosting, and weighting are strategies which can be introduced to help deal with the situation.
Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.
Those are economics terms that are not frequently asked of Data Scientists but they are useful to know. Price optimization is the use of mathematical tools to determine how customers will respond to different prices for its products and services through different channels. Big Data and data mining enables use of personalization for price optimization. Now companies like Amazon can even take optimization further and show different prices to different visitors, based on their history, although there is a strong debate about whether this is fair. Price elasticity in common usage typically refers to Price elasticity of demand, a measure of price sensitivity. It is computed as: Price Elasticity of Demand = % Change in Quantity Demanded / % Change in Price. Similarly, Price elasticity of supply is an economics measure that shows how the quantity supplied of a good or service responds to a change in its price. Inventory management is the overseeing and controlling of the ordering, storage and use of components that a company will use in the production of the items it will sell as well as the overseeing and controlling of quantities of finished products for sale. Wikipedia defines Competitive intelligence as the action of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors, and any aspect of the environment needed to support executives and managers making strategic decisions for an organization. Tools like Google Trends, Alexa, Compete, can be used to determine general trends and analyze your competitors on the web.
What is statistical power?
Wikipedia defines Statistical power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true. To put in another way, Statistical power is the likelihood that a study will detect an effect when the effect is present. The higher the statistical power, the less likely you are to make a Type II error (concluding there is no effect when, in fact, there is).