10+ White and Associates Interview Questions and Answers

Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.

• Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.

• Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.

• Linear Regression assumes a linear relationship between the variables...read more

I chose Data Science field because of its potential to solve complex problems and make a positive impact on society.

• Fascination with data and its potential to drive insights

• Desire to solve complex problems and make a positive impact on society

• Opportunity to work with cutting-edge technology and tools

• Ability to work in a variety of industries and domains

• Examples: Predictive maintenance in manufacturing, fraud detection in finance, personalized medicine in healthcare

No, confusion matrix is not used in Linear Regression.

• Confusion matrix is used to evaluate classification models.

• Linear Regression is a regression model, not a classification model.

• Evaluation metrics for Linear Regression include R-squared, Mean Squared Error, etc.

Machine learning enables computers to learn from data and make predictions or decisions without being explicitly programmed.

• Machine learning can automate and optimize complex processes

• It can help identify patterns and insights in large datasets

• It can improve accuracy and efficiency in decision-making

• Examples include image recognition, natural language processing, and predictive analytics

• It can also be used for anomaly detection and fraud prevention

Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy.

• Random Forest is a type of supervised learning algorithm used for classification and regression tasks.

• It creates multiple decision trees and combines their outputs to make a final prediction.

• Each decision tree is built using a random subset of features and data points to reduce overfitting.

• Random Forest is more accurate than a single decision tree an...read more

KNN is a non-parametric algorithm used for classification and regression tasks.

• KNN stands for K-Nearest Neighbors.

• It works by finding the K closest data points to a given test point.

• The class or value of the test point is then determined by the majority class or average value of the K neighbors.

• KNN can be used for both classification and regression tasks.

• It is a simple and easy-to-understand algorithm, but can be computationally expensive for large datasets.

Using stochastic to analyze Indian cricket team's current scenario and eliminate worst while preserving best.

• Stochastic analysis can help identify the strengths and weaknesses of individual players and the team as a whole

• It can also help in predicting the outcome of future matches based on past performance

• Eliminating the worst performers and preserving the best can help improve the team's overall performance

• For example, if a player consistently performs poorly in a particular...read more

Confusion matrix is a table used to evaluate the performance of a classification model.

• It is a 2x2 matrix that shows the number of true positives, false positives, true negatives, and false negatives.

• It helps in calculating various metrics like accuracy, precision, recall, and F1 score.

• It is useful in identifying the strengths and weaknesses of a model and improving its performance.

• Example: In a binary classification problem, the confusion matrix can be used to evaluate how m...read more

To build a business case without resources for a prototype, focus on customer validation, market research, and potential ROI.

• Conduct market research to gather data on potential demand, target market, and competitors.

• Identify and reach out to potential alpha customers to gauge interest and gather feedback.

• Create a detailed business plan outlining the problem, solution, target market, competitive landscape, and potential ROI.

• Present the business case to management, highlighting...read more

TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.

• TF-IDF stands for Term Frequency-Inverse Document Frequency

• It is used in Natural Language Processing (NLP) to determine the importance of a word in a document

• TF-IDF is calculated by multiplying the term frequency (TF) by the inverse document frequency (IDF)

• It helps in identifying the most important words in a document

ML models can be evaluated using metrics like accuracy, precision, recall, F1 score. Bagging combines multiple models, while boosting focuses on correcting errors. F1 score balances precision and recall.

• ML models can be evaluated using metrics like accuracy, precision, recall, and F1 score.

• Bagging is an ensemble technique where multiple models are trained independently and then combined by averaging or voting.

• Boosting is an ensemble technique where models are trained sequenti...read more

To connect ADLS Gen2 with Databricks, you can use Azure Blob Storage and set up a linked service in Databricks.

• Create an Azure Blob Storage account in the Azure portal

• Set up a linked service in Databricks to connect to the Azure Blob Storage account

• Use the Azure Blob Storage account as the storage account for Databricks to access ADLS Gen2 data

Proba method is used to calculate class probability in decision trees. Machine learning models are evaluated using metrics like accuracy, precision, recall, and F1 score.

• Proba method calculates the probability of a class label in decision trees by counting the occurrences of each class in a leaf node and dividing by the total number of samples in that node.

• Class probability in decision trees is calculated based on the proportion of samples in a leaf node that belong to each c...read more

TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.

• TF-IDF stands for Term Frequency-Inverse Document Frequency.

• It is used in Natural Language Processing (NLP) to determine the importance of a word in a document.

• TF-IDF is calculated by multiplying the term frequency (TF) of a word by the inverse document frequency (IDF) of the word.

• It helps in identifying the most important words in a document by giving hi...read more

I would evaluate the impact on the overall product strategy, prioritize based on customer needs and market trends, and communicate effectively with stakeholders.

• Evaluate the impact of the feature request on the overall product strategy

• Prioritize the request based on customer needs and market trends

• Communicate effectively with stakeholders to discuss the potential impact and trade-offs

• Consider if the request aligns with the long-term vision and goals of the product

• Explore alte...read more

I worked on a data project for a proposal writing for a client in the healthcare industry.

• Analyzed patient data to identify trends and insights

• Developed a predictive model to forecast patient outcomes

• Created visualizations to communicate findings effectively