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I applied via Company Website and was interviewed before Feb 2023. There was 1 interview round.
I applied via Walk-in and was interviewed in Mar 2020. There was 1 interview round.
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 t...
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.
Fea...
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.
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 multicollinear...
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 reduc...
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 commo...
Gini coefficient measures the inequality among values of a frequency distribution.
Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.
It is commonly used to measure income inequality in a population.
A Gini coefficient of 0.4 or higher is considered to be a high level of inequality.
Gini coefficient can be calculated using the Lorenz curve, which plots the cumulati...
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 c...
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
Top trending discussions
I applied via Campus Placement and was interviewed in Dec 2022. There were 4 interview rounds.
Hard level aptitude questions... Time taking sums
Step function is a function that returns a constant value for a certain range of inputs.
In machine learning, step functions are used as activation functions in neural networks.
They are typically used in binary classification problems where the output is either 0 or 1.
Examples include Heaviside step function and sigmoid step function.
Investigate the model performance metrics and adjust the threshold for classification.
Analyze the confusion matrix to understand the distribution of false positives.
Adjust the threshold for classification to reduce false positives.
Consider using different evaluation metrics like precision, recall, and F1 score.
Explore feature importance to identify variables contributing to false positives.
I applied via Job Portal and was interviewed in Aug 2023. There were 2 interview rounds.
Aptitude test for about an hour.
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
Number of trees: The number of decision trees to be used in the random forest.
Maximum depth of trees: The maximum depth allowed for each decision tree.
Minimum samples split: The minimum number of samples required to split a node.
Maximum features: The maximum number of features to consider when
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.
Normal aptitude questions
I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.
Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
Sigmoid function is commonly used in machine learning for binary classification problems.
It is defined as f(x) = 1 / (1 + e^(-x)), where e is the base of the natural logarithm.
The output of the sigmoid function is always in the range (0, 1).
It is used to convert a continuous input into a probability value.
Example: f(0) = 0.5
A T-test in logistic regression is used to determine the significance of individual predictor variables.
T-test in logistic regression is used to test the significance of individual coefficients of predictor variables.
It helps in determining whether a particular predictor variable has a significant impact on the outcome variable.
The null hypothesis in a T-test for logistic regression is that the coefficient of the predi...
To fit a model to an unexplored market, conduct thorough market research, gather relevant data, identify key variables, test different models, and continuously iterate and refine the model.
Conduct thorough market research to understand the dynamics of the unexplored market
Gather relevant data on customer behavior, market trends, competition, etc.
Identify key variables that may impact the market and model outcomes
Test d...
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