BinaryRoots Interview Questions and Answers

The Wald test is used in logistic regression to check the significance of the variable.

• The Wald test calculates the ratio of the estimated coefficient to its standard error.

• It follows a chi-square distribution with one degree of freedom.

• A small p-value indicates that the variable is significant.

• For example, in Python, the statsmodels library provides the Wald test in the summary of a logistic regression model.

R square is a statistical measure that represents the proportion of the variance in the dependent variable explained by the independent variables.

• R square is a value between 0 and 1, where 0 indicates that the independent variables do not explain any of the variance in the dependent variable, and 1 indicates that they explain all of it.

• It is used to evaluate the goodness of fit of a regression model.

• Adjusted R square takes into account the number of predictors in the model an...read more

Outliers can be detected using statistical methods like box plots, z-score, and IQR. Treatment can be removal or transformation.

• Use box plots to visualize outliers

• Calculate z-score and remove data points with z-score greater than 3

• Calculate IQR and remove data points outside 1.5*IQR

• Transform data using log or square root to reduce the impact of outliers

Multicollinearity in logistic regression can be checked using correlation matrix and variance inflation factor (VIF).

• Calculate the correlation matrix of the independent variables and check for high correlation coefficients.

• Calculate the VIF for each independent variable and check for values greater than 5 or 10.

• Consider removing one of the highly correlated variables or variables with high VIF to address multicollinearity.

• Example: If variables A and B have a correlation coeff...read more

Variable reducing techniques are methods used to identify and select the most relevant variables in a dataset.

• Variable reducing techniques help in reducing the number of variables in a dataset.

• These techniques aim to identify the most important variables that contribute significantly to the outcome.

• Some common variable reducing techniques include feature selection, dimensionality reduction, and correlation analysis.

• Feature selection methods like backward elimination, forward ...read more

Bagging and boosting are ensemble methods used in machine learning to improve model performance.

• Bagging involves training multiple models on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves iteratively training models on the same dataset, with each subsequent model focusing on the samples that were misclassified by the previous model.

• Bagging reduces variance and overfitting, while boosting reduces bias a...read more

A chair is a piece of furniture used for sitting, while a cart is a vehicle used for transporting goods.

• A chair typically has a backrest and armrests, while a cart does not.

• A chair is designed for one person to sit on, while a cart can carry multiple items or people.

• A chair is usually stationary, while a cart is mobile and can be pushed or pulled.

• A chair is commonly found in homes, offices, and public spaces, while a cart is often used in warehouses, supermarkets, and farms.

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 a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more predictor variables.

• It uses a logistic function to model the probability of the dependent variable taking a particular value.

• It is commonly used in machine learning for classification problems, ...read more