by: Ritesh SinhaPosted on:

Cognizant Interview Question Machine Learning

Here’s a list of important interview questions in statistics that are commonly asked in the context of Machine Learning:

1. Descriptive Statistics

2. Probability

  • What is the difference between probability and likelihood?
  • Explain Bayes’ Theorem with an example.
  • What are conditional probability and joint probability?
  • What is the Law of Large Numbers?
  • What are prior, likelihood, and posterior probabilities?

3. Probability Distributions

  • Explain the difference between a discrete and continuous probability distribution.
  • What is a normal distribution, and why is it important in statistics?
  • Explain the Central Limit Theorem and its significance.
  • What is the difference between a uniform distribution and a normal distribution?
  • Describe the binomial distribution and provide a practical example.

4. Hypothesis Testing

  • What is a null hypothesis and an alternative hypothesis?
  • Explain p-value and its significance in hypothesis testing.
  • What are Type I and Type II errors?
  • What is a confidence interval, and how is it constructed?
  • Explain the concept of statistical power in hypothesis testing.

5. Correlation and Regression

  • What is the difference between correlation and causation?
  • How is correlation coefficient interpreted?
  • Explain the concept of multicollinearity in regression.
  • What is the difference between linear and logistic regression?
  • How do you interpret the coefficients in a linear regression model?

6. Sampling and Estimation

  • What is the difference between a sample and a population?
  • Explain different sampling techniques like random sampling, stratified sampling, and cluster sampling.
  • What is the difference between point estimation and interval estimation?
  • What is bias in sampling, and how can it affect your results?
  • How do you ensure that a sample is representative of the population?

7. ANOVA (Analysis of Variance)

  • What is ANOVA, and when is it used?
  • What are the assumptions of ANOVA?
  • Explain the difference between one-way and two-way ANOVA.
  • What is the F-statistic in ANOVA, and how is it interpreted?
  • When would you use an ANOVA test instead of a t-test?

8. Chi-Square Test

  • What is the Chi-Square test, and when is it used?
  • How do you interpret the results of a Chi-Square test?
  • What are the assumptions of the Chi-Square test?
  • Explain the difference between a Chi-Square test for independence and a Chi-Square goodness-of-fit test.

9. Time Series Analysis

  • What is stationarity in a time series, and why is it important?
  • Explain autocorrelation and partial autocorrelation in time series.
  • What is ARIMA, and how is it used in time series forecasting?
  • What are trend, seasonality, and noise in time series data?
  • How do you handle missing values in time series data?

10. Decision Trees and Random Forest

  • How does a decision tree make decisions?
  • Explain Gini impurity and entropy in the context of decision trees.
  • What is overfitting, and how can it be prevented in decision trees?
  • How does a random forest model reduce the risk of overfitting?
  • What are the advantages and disadvantages of using decision trees?

11. Bias-Variance Tradeoff

  • What is the bias-variance tradeoff?
  • Explain how bias and variance affect the model performance.
  • How do you reduce high variance or high bias in a model?
  • What is cross-validation, and how does it help in managing bias-variance tradeoff?

12. Model Evaluation Metrics

  • What is the difference between accuracy, precision, and recall?
  • Explain the ROC curve and AUC.
  • What is F1 score, and when should it be used?
  • How do you evaluate a classification model?
  • What is the confusion matrix, and how is it interpreted?

13. Clustering

  • What is clustering, and when is it used?
  • Explain the difference between k-means and hierarchical clustering.
  • How do you determine the optimal number of clusters in k-means clustering?
  • What is the silhouette score in clustering?
  • Explain the difference between supervised and unsupervised learning with examples.

These questions cover a wide range of topics in statistics that are fundamental to Machine Learning and Data Science. Preparing well for these questions will give you a solid foundation for technical interviews in this field.