Machine Learning Interview Question Sets
Certainly! Here’s a collection of 50 statistics interview questions along with their answers that are relevant for a machine learning interview:
1. Q: What is the difference between descriptive and inferential statistics?
A: Descriptive statistics summarize and describe the features of a dataset. Inferential statistics use a random sample of data taken from a population to make inferences about the population.
2. Q: What is the central limit theorem (CLT)?
A: The CLT states that the distribution of the sample mean will approximate a normal distribution as the sample size becomes larger, regardless of the population’s distribution, provided the samples are independent and identically distributed.
3. Q: What are the types of sampling methods?
A: Common types of sampling methods include simple random sampling, stratified sampling, cluster sampling, systematic sampling, and convenience sampling.
4. Q: What is a p-value?
A: A p-value measures the strength of evidence against the null hypothesis. It is the probability of obtaining a result at least as extreme as the observed one, assuming the null hypothesis is true.
5. Q: What is the difference between Type I and Type II errors?
A: Type I error occurs when the null hypothesis is rejected when it is actually true (false positive). Type II error occurs when the null hypothesis is not rejected when it is actually false (false negative).
6. Q: What is a confidence interval?
A: A confidence interval is a range of values derived from a dataset that is likely to contain the true population parameter with a specified level of confidence (e.g., 95%).
7. Q: Explain the concept of statistical power.
A: Statistical power is the probability that a test correctly rejects a false null hypothesis (i.e., it detects an effect when there is one). It is affected by sample size, effect size, significance level, and variability.
8. Q: What is correlation and how is it different from causation?
A: Correlation measures the strength and direction of a linear relationship between two variables. Causation indicates that one variable directly affects another. Correlation does not imply causation.
9. Q: What is the difference between a one-tailed and a two-tailed test?
A: A one-tailed test tests for the possibility of the relationship in one direction, while a two-tailed test tests for the relationship in both directions.
10. Q: What is an outlier and how can it be detected?
A: An outlier is a data point significantly different from others. It can be detected using methods like the Z-score, IQR (Interquartile Range), or visualization techniques like box plots.
11. Q: What are the assumptions of linear regression?
A: The assumptions include linearity, independence, homoscedasticity (constant variance of errors), normality of errors, and no multicollinearity among predictors.
12. Q: Explain heteroscedasticity and how to detect it.
A: Heteroscedasticity occurs when the variance of errors varies across observations. It can be detected using residual plots, Breusch-Pagan test, or White’s test.
13. Q: What is multicollinearity and how can it be addressed?
A: Multicollinearity occurs when predictor variables are highly correlated, leading to unreliable coefficient estimates. It can be addressed by removing highly correlated predictors, combining them, or using techniques like Principal Component Analysis (PCA).
14. Q: Explain the difference between parametric and non-parametric tests.
A: Parametric tests assume underlying statistical distributions (e.g., t-test, ANOVA), while non-parametric tests do not (e.g., Mann-Whitney U test, Kruskal-Wallis test).
15. Q: What is a normal distribution?
A: A normal distribution is a bell-shaped, symmetric distribution characterized by its mean and standard deviation, where most of the data falls within three standard deviations from the mean.
16. Q: Explain the concept of skewness.
A: Skewness measures the asymmetry of the distribution of values. Positive skewness indicates a distribution with a longer right tail, and negative skewness indicates a longer left tail.
17. Q: What is kurtosis?
A: Kurtosis measures the “tailedness” of the distribution. High kurtosis indicates heavy tails, while low kurtosis indicates light tails compared to a normal distribution.
18. Q: What is the law of large numbers?
A: The law of large numbers states that as the size of a sample increases, the sample mean will get closer to the population mean.
19. Q: What is the difference between population and sample?
A: A population includes all members of a specified group, while a sample is a subset of the population used to make inferences about the population.
20. Q: What are degrees of freedom in statistics?
A: Degrees of freedom refer to the number of independent values that can vary in an analysis without violating any constraints.
21. Q: What is a hypothesis test?
A: A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
22. Q: What is ANOVA (Analysis of Variance)?
A: ANOVA is a statistical method used to compare means across multiple groups to determine if at least one group mean is significantly different from the others.
23. Q: What is the purpose of a chi-square test?
A: A chi-square test assesses the association between categorical variables and checks if observed frequencies match expected frequencies.
24. Q: What is logistic regression?
A: Logistic regression models the probability of a binary outcome based on one or more predictor variables.
25. Q: What is the purpose of a z-test?
A: A z-test determines whether there is a significant difference between sample and population means when the population variance is known.
26. Q: Explain the difference between a z-test and a t-test.
A: A z-test is used when the population variance is known and the sample size is large, while a t-test is used when the population variance is unknown and/or the sample size is small.
27. Q: What is the difference between a histogram and a bar chart?
A: A histogram displays the frequency distribution of continuous data, with adjacent bars, while a bar chart shows categorical data with separated bars.
28. Q: What is a time series analysis?
A: Time series analysis involves analyzing data points collected or recorded at specific time intervals to identify trends, cycles, and seasonal variations.
29. Q: What is the purpose of cross-validation?
A: Cross-validation evaluates the performance of a model by partitioning the data into training and validation sets multiple times to ensure the model’s robustness.
30. Q: What is bootstrapping?
A: Bootstrapping is a resampling technique used to estimate the distribution of a statistic by repeatedly sampling with replacement from the original data.
31. Q: Explain the difference between bias and variance.
A: Bias is the error introduced by approximating a real-world problem, which may oversimplify the model. Variance is the error introduced by the model’s sensitivity to small fluctuations in the training set.
32. Q: What is a ROC curve?
A: A ROC (Receiver Operating Characteristic) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system by plotting the true positive rate against the false positive rate.
33. Q: What is AUC (Area Under the Curve)?
A: AUC represents the area under the ROC curve and provides a single measure of a model’s performance. A higher AUC indicates better model performance.
34. Q: What is the difference between precision and recall?
A: Precision is the ratio of true positive predictions to the total predicted positives, while recall (sensitivity) is the ratio of true positive predictions to the actual positives in the data.
35. Q: What is the F1 score?
A: The F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics. It is useful for imbalanced datasets.
36. Q: What is regularization and why is it used?
A: Regularization adds a penalty to the model to prevent overfitting by discouraging complex models. Techniques include L1 (Lasso) and L2 (Ridge) regularization.
37. Q: What is the difference between bagging and boosting?
A: Bagging (Bootstrap Aggregating) reduces variance by training multiple models on different subsets of data and averaging their predictions. Boosting reduces bias by sequentially training models to correct errors made by previous models.
38. Q: What is the purpose of a validation set?
A: A validation set is used to tune model parameters and evaluate the model’s performance during the training phase to prevent overfitting.
39. Q: What is the difference between supervised and unsupervised learning?
A: Supervised learning involves training a model on labeled data, while unsupervised learning involves finding patterns and relationships in unlabeled data.
40. Q: What is a confusion matrix?
A: A confusion matrix is a table used to evaluate the performance of a classification model by showing the true positives, true negatives, false positives, and false negatives.
41. Q: What is the purpose of normalization or standardization?
A: Normalization or standardization scales data to a common range or distribution to ensure that features contribute equally to the model’s performance.
42. Q: What is the curse of dimensionality?
A: The curse of dimensionality refers to the difficulties and challenges that arise when analyzing and organizing data in high-dimensional spaces, often leading to overfitting and increased computational complexity.
43. Q: What is a likelihood function?
A: A likelihood function estimates the probability of observing the given data under different parameter values of a statistical model.
44. Q: What is Bayesian inference?
A: Bayesian inference is a method of statistical inference in which Bayes’ theorem is used to update the probability of a hypothesis as more evidence or information becomes available.
45. Q: What is the difference between parametric and non-parametric models?
A: Parametric models assume a specific form for the underlying distribution and have a finite number of parameters. Non-parametric models do not assume a specific distribution and have an infinite number of parameters.
46. Q: What is the importance of feature selection?
A: Feature selection improves model performance by reducing overfitting, decreasing training time, and enhancing generalization by selecting the most relevant features.
47. Q: What is PCA (Principal Component Analysis)?
A: PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space by identifying the principal components that capture the most variance in the data.
48. Q: What is the difference between hard and soft classification?
A: Hard classification assigns a definitive class label to each data point, while soft classification provides the probabilities of belonging to each class.
49. Q: Explain the concept of ensemble learning.
A: Ensemble learning combines multiple models to improve overall performance by leveraging the strengths of each individual model. Examples include bagging, boosting, and stacking.
50. Q: What is the k-nearest neighbors (k-NN) algorithm?
A: k-NN is a non-parametric, instance-based learning algorithm that classifies a data point based on the majority class among its k-nearest neighbors in the feature space.
These questions and answers cover a wide range of foundational concepts in statistics and machine learning, helping you prepare for various aspects of a machine learning interview.
