Essjay Ericsson 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.

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

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