20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1
Can ChatGPT provide answers to data science questions to the same standard of humans? Check out this attempt to do so, and compare the answers to those from experts.
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7 years ago this month, KDnuggets published an article titled 20 Questions to Detect Fake Data Scientists, written by Andrew Fogg. The following month, KDnuggets editors collectively answered the questions in the subsequent article 21 Must-Know Data Science Interview Questions and Answers.
Looking to utilize ChatGPT in new and exciting ways — to both learn more about ChatGPT itself, and learn about data science interview question topics — we decided to resurrect those same questions on the septennial anniversary of the original, and pose them to ChatGPT.
I will preface this article with the clear statement that all of the answers to the questions in this article have been provided by ChatGPT. Do with that information what you will.
I would encourage readers to compare these answers with those provided by the KDnuggets editors in 2016, in order to see which answers are more thorough, which are more accurate, and which just read better. Feel free to criticize particular answers or the entire project in the comments below.
Now let's have a look to see how well ChatGPT does at answering the following 10 questions that we still agree would be great candidates for detecting fake data scientists applying for a job in your organization.
1. Explain what regularization is and why it is useful.
Regularization is a technique used in machine learning to prevent overfitting. Overfitting occurs when a model is trained too well on the training data, and as a result, it performs poorly on unseen data. Regularization is a method to discourage large weights in the model by adding a penalty term to the loss function. This penalty term is a coefficient multiplied by the magnitude of the weights. By adding this term, the model is forced to have smaller weights, which helps to reduce overfitting and improve the generalization of the model. Two common types of regularization are L1 and L2 regularization. L1 regularization adds a penalty term that is proportional to the absolute value of the weights, and L2 regularization adds a penalty term that is proportional to the square of the weights.
2. Which data scientists do you admire most? which startups?
As a language model, I do not have personal preferences or admiration. However, I can tell you some names of data scientists and startups that are well-known and respected in the field.
In terms of data scientists, some notable figures include:
- Yann LeCun, a pioneer in the field of deep learning and computer vision.
- Andrew Ng, a pioneer in the field of online education and a leading expert in machine learning.
- Geoffrey Hinton, a pioneer in the field of deep learning and neural networks.
- Kai-Fu Lee, a leading AI expert, investor and author.
- Yoshua Bengio, a pioneer in the field of deep learning and natural language processing.
In terms of startups, some notable companies include:
- OpenAI, an artificial intelligence research lab and startup that aims to build safe AI and ensure its benefits are widely shared.
- Google Brain, which is a deep learning research team at Google.
- DeepMind, a British artificial intelligence company founded in September 2010, and acquired by Alphabet Inc in 2015.
- Nervana, a startup that is building specialized hardware to accelerate deep learning workloads.
- NVIDIA, a company that specializes in graphics processing units (GPUs) and has been at the forefront of the deep learning revolution with their GPUs optimized for deep learning workloads.
3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
There are several ways to validate a model created using multiple regression for predicting a quantitative outcome variable. Some common methods include:
- Holdout method: This involves randomly splitting the data into training and testing sets, and using the training set to fit the model and the testing set to evaluate its performance.
- k-fold cross-validation: This method involves randomly dividing the data into k folds, training the model on k-1 folds and using the remaining fold as the testing set, repeating the process k times and averaging the results.
- Bootstrapping: This method involves repeatedly sampling the data with replacement to create multiple "pseudo-samples" and using these samples to fit and evaluate the model.
- R^2 and adjusted R^2: These are statistical measures that indicate the proportion of the variance in the outcome variable that is explained by the predictor variables.
- Root mean squared error (RMSE) or mean absolute error (MAE): These are used to measure the difference between the predicted and actual values.
- Regression diagnostic plots: These are used to check for the assumptions of linearity, normality, homoscedasticity and independence of errors, and the presence of outliers and leverage points.
- Comparing with a Benchmark model: Comparing the model with a simple benchmark model like mean or median can give an idea of how well the model is performing.
It is important to use multiple methods to validate a model, as each method can reveal different types of errors or issues with the model.
4. Explain what precision and recall are. How do they relate to the ROC curve?
Precision and recall are two important measures of a binary classifier's performance.
Precision is the proportion of true positive predictions (correctly predicted positive examples) out of all positive predictions made by the classifier. It is a measure of the classifier's ability to correctly identify positive examples.
Recall (also known as sensitivity or true positive rate) is the proportion of true positive predictions out of all actual positive examples. It is a measure of the classifier's ability to identify all positive examples.
The Receiver Operating Characteristic (ROC) curve is a graphical representation of a classifier's performance. It plots the true positive rate (recall) against the false positive rate (1-specificity) for different thresholds of the classifier. The area under the ROC curve (AUC) is a measure of the classifier's overall performance.
Precision and recall are closely related to the ROC curve. A classifier with high precision and high recall will have a high true positive rate and a low false positive rate, resulting in a ROC curve that hugs the top left corner of the plot. A classifier with low precision and low recall will have a low true positive rate and a high false positive rate, resulting in a ROC curve that is close to the diagonal line.
In practice, it is often necessary to trade off precision and recall to achieve a balance between the two. This can be done by adjusting the threshold of the classifier, which controls the balance between true positives and false positives. A higher threshold will result in higher precision but lower recall, while a lower threshold will result in lower precision but higher recall.
5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
To prove that an improvement to an algorithm is real and not just due to chance, you can use statistical hypothesis testing. This involves formulating a null hypothesis and an alternative hypothesis, and then using a test statistic and a significance level to decide whether to reject or fail to reject the null hypothesis.
The null hypothesis is usually that the new algorithm is no better than the old one or the baseline algorithm, and the alternative hypothesis is that the new algorithm is better.
A common way to perform the hypothesis test is by using a two-tailed t-test. This compares the means of the performance metrics of the new and old algorithms and tests whether they are significantly different. Another way is to use a paired sample test, where the same dataset is used for both the new and old algorithm, and the results are compared.
If the p-value (the probability of obtaining a test statistic as extreme or more extreme than the one observed, assuming the null hypothesis is true) is less than the significance level (usually 0.05), the null hypothesis is rejected and it is concluded that the new algorithm is better.
Additionally, to ensure that the improvement is not just due to chance, you can use techniques such as cross-validation, bootstrapping and repeat the test multiple times with different random subsets of the data to increase the robustness of the results.
It's also important to compare the performance of the new algorithm to the state-of-the-art algorithms in the same field, if possible, to give a better perspective on the improvement.
6. What is root cause analysis?
Root cause analysis (RCA) is a method used to identify the underlying cause(s) of a problem or incident. It is a systematic process that involves identifying, analyzing, and resolving the problem at its source, rather than just treating the symptoms. The goal of RCA is to prevent similar problems from occurring in the future by identifying and addressing the root causes of the problem.
RCA typically involves the following steps:
- Define the problem: Clearly identify the problem and its impact on the organization or system.
- Gather data: Collect data on the problem, including symptoms, timing, location, and any relevant information.
- Analyze the data: Use tools such as cause-and-effect diagrams, Pareto charts, and fishbone diagrams to identify the possible causes of the problem.
- Identify the root cause: Use the data and analysis to identify the most likely cause(s) of the problem.
- Develop solutions: Develop and implement solutions to address the root cause(s) of the problem.
- Verify the solution: Monitor and evaluate the effectiveness of the solutions to ensure that the problem has been resolved.
- RCA can be applied to a wide range of problems, from small-scale incidents to large-scale accidents and crises, in both manufacturing and service industries, as well as in healthcare, aviation, and other fields.
It's important to note that RCA should be performed by a skilled and experienced team, and that it should be conducted in an objective and impartial manner, avoiding any blame or finger-pointing.
7. Are you familiar with pricing optimization, price elasticity, inventory management, competitive intelligence? Give examples.
Yes, I am familiar with pricing optimization, price elasticity, inventory management, and competitive intelligence.
Pricing optimization is the process of determining the optimal price for a product or service to maximize revenue or profit.
Price elasticity is a measure of how the demand for a product or service changes as its price changes.
Inventory management is the process of managing the inventory of a business, including the control and movement of stock and the forecasting of future demand.
Competitive intelligence is the process of gathering, analyzing, and interpreting information about competitors, competitors' products, and the competitive environment to gain a competitive advantage.
- A clothing retailer uses pricing optimization to set the price of a popular sweater at $50, the price at which they can sell the most sweaters and make the most profit.
- A company selling a new product with a high price elasticity, meaning a small change in price causes a large change in demand, may adjust the price of the product to increase or decrease demand.
- A retail store uses inventory management to ensure they have enough stock of a popular toy during the holiday season and avoid stockouts
- A company uses competitive intelligence to research its competitors' pricing, product offerings, and marketing strategies to inform its own business decisions.
8. What is statistical power?
Statistical power is the probability that a statistical test will correctly reject the null hypothesis (i.e., detect an effect) when the alternative hypothesis is true. In other words, it is the ability of a test to detect a difference or effect when one truly exists. It is usually expressed as a number between 0 and 1 and is often denoted by the Greek letter beta (β). A higher statistical power means that the test is more likely to detect a true difference or effect, while a lower statistical power means that the test is more likely to fail to detect a true difference or effect.
Statistical power is influenced by several factors, including the sample size, the significance level (alpha), and the effect size. In general, increasing the sample size, decreasing the significance level, and increasing the effect size will increase the statistical power of a test.
It is important to note that a high statistical power does not guarantee that a test will detect a true difference or effect but it is a good indication of the test's sensitivity to detect the effect, it is also important to understand that a high statistical power doesn't indicate high accuracy or reliability on the test.
9. Explain what resampling methods are and why they are useful. Also explain their limitations.
Resampling methods are statistical techniques that involve repeatedly drawing samples from a dataset and using those samples to make inferences about the population from which the dataset was drawn. There are several types of resampling methods, including:
- Bootstrapping: This method involves randomly drawing samples from the original dataset with replacement. The samples are then used to estimate population statistics such as means, standard deviations, and confidence intervals.
- Cross-validation: This method is used to estimate the performance of a predictive model. It involves dividing the original dataset into subsets or "folds", training the model on some of the subsets and testing it on the remaining subsets. This process is repeated multiple times to get an estimate of the model's performance.
- Permutation testing: This method is used to test hypotheses about the independence of two variables. It involves randomly permuting the values of one variable and measuring the effect of the permutation on the relationship between the two variables.
Resampling methods are useful because they do not rely on assumptions about the underlying distribution of the population, they are more robust than traditional parametric methods. They allow researchers to make inferences about a population using only the information that is available in the sample, which is particularly useful when the population is difficult or expensive to measure.
However, resampling methods do have some limitations. One limitation is that the results obtained from these methods may be sensitive to the specific sample that is used, and so the results may not be generalizable to the population as a whole. Additionally, when the sample size is small, these methods may be less powerful than traditional parametric methods. Also, when the sample size is small, these methods may not be able to detect small differences or effects.
10. Is it better to have too many false positives, or too many false negatives? Explain.
Whether it is better to have too many false positives or too many false negatives depends on the specific context and the desired outcome of the analysis.
False positives, also known as type I errors, occur when a test or analysis incorrectly suggests that an effect or difference is present when it is actually not. In general, false positives are considered less desirable in situations where a negative outcome (e.g. a patient does not have a disease) has more severe consequences than a positive outcome (e.g. a patient has a disease but is not treated).
False negatives, also known as type II errors, occur when a test or analysis incorrectly suggests that an effect or difference is not present when it is actually present. In general, false negatives are considered less desirable in situations where a positive outcome (e.g. a patient has a disease and is treated) has more severe consequences than a negative outcome (e.g. a patient does not have a disease but is not treated).
For example, in a medical context, a false negative may result in a patient not receiving treatment for a serious illness, while a false positive may lead to unnecessary treatment and its associated risks. On the other hand, in a security context, a false negative may result in an attacker getting through security, while a false positive may lead to inconvenience for a legitimate user.
It is important to set the threshold that balance these trade-offs, and this is where the concepts of sensitivity and specificity come in. Sensitivity is the proportion of true positives (correctly identified cases) among all the actual positive cases, while specificity is the proportion of true negatives (correctly identified non-cases) among all the actual negative cases.
In summary, the choice between too many false positives or too many false negatives depends on the specific context, and the desired outcome, and it's important to consider both the costs and benefits of each type of error in order to make an informed decision.
That's the first half of the 20 questions from the original publication. We will bring you the second half in the coming weeks, so keep your eyes open.
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.