10+ TVS Sundram Fasteners Interview Questions and Answers

Logistic regression is used for binary classification, while linear regression is used for predicting continuous values.

• Logistic regression is a classification algorithm, while linear regression is a regression algorithm.

• Logistic regression uses a logistic function to model the probability of the binary outcome.

• Linear regression uses a linear function to model the relationship between the independent and dependent variables.

• Logistic regression predicts discrete outcomes (e.g....read more

Random forest is an ensemble learning method that uses multiple decision trees to improve prediction accuracy.

• Random forest builds multiple decision trees and combines their predictions to reduce overfitting.

• Decision trees are prone to overfitting and can be unstable, while random forest is more robust.

• Random forest can handle missing values and categorical variables better than decision trees.

• Example: Random forest can be used for predicting customer churn in a telecom compa...read more

The formula of logistic regression is a mathematical equation used to model the relationship between a binary dependent variable and one or more independent variables.

• The formula is: log(odds) = β0 + β1x1 + β2x2 + ... + βnxn

• The dependent variable is transformed using the logit function to obtain the log-odds ratio.

• The independent variables are multiplied by their respective coefficients (β) and summed up with the intercept (β0).

• The resulting value is then transformed back to ...read more

Model accuracy can be measured using metrics such as confusion matrix, ROC curve, and precision-recall curve.

• Confusion matrix shows true positives, true negatives, false positives, and false negatives.

• ROC curve plots true positive rate against false positive rate.

• Precision-recall curve plots precision against recall.

• Other metrics include accuracy, F1 score, and AUC-ROC.

• Cross-validation can also be used to evaluate model performance.

Specificity and sensitivity are statistical measures used to evaluate the performance of a binary classification model.

• Specificity measures the proportion of true negatives correctly identified by the model.

• Sensitivity (also known as recall or true positive rate) measures the proportion of true positives correctly identified by the model.

• Both measures are commonly used in medical diagnostics to assess the accuracy of tests or models.

• Specificity and sensitivity are often used ...read more

AUC-ROC curve is a graphical representation of the performance of a classification model.

• AUC-ROC stands for Area Under the Receiver Operating Characteristic curve.

• It is used to evaluate the performance of binary classification models.

• The curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various classification thresholds.

• AUC-ROC ranges from 0 to 1, with a higher value indicating better model performance.

• An AUC-ROC of 0.5 repres...read more

t-test is a statistical test used to determine if there is a significant difference between the means of two groups.

• It compares the means of two groups and assesses if the difference is statistically significant.

• It is commonly used in hypothesis testing and comparing the effectiveness of different treatments or interventions.

• There are different types of t-tests, such as independent samples t-test and paired samples t-test.

• The t-test calculates a t-value and p-value, where the...read more

Linear regression is a statistical method used to model the relationship between two variables.

• It assumes a linear relationship between the dependent and independent variables.

• It is used to predict the value of the dependent variable based on the value of the independent variable.

• It can be used for both simple and multiple regression analysis.

• Example: predicting the price of a house based on its size or predicting the salary of an employee based on their years of experience.

A random forest is an ensemble learning method that combines multiple decision trees to make predictions.

• Random forest is a supervised learning algorithm.

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

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

• Each decision tree is trained on a random subset of the training data and features.

• Random forest reduces overfitting and improves accuracy compared to a single decision tree...read more

Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

• It is used to predict the probability of a binary outcome (0 or 1).

• It is a type of regression analysis that uses a logistic function to model the relationship between the dependent and independent variables.

• It is commonly used in machine learning and data analysis for classification problems.

• Example: predicting whethe...read more