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Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).
It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.
A Gini coefficient of 0.5 indicates a model that is no better than random guessing.
Commonly used in credit
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
Specify parameters for XGBoost model such as learning rate, max depth, and number of trees
Split data into training and validation sets using train_test_split function
Fit the XGBoost model on training data using fit method
Tune hyperparameters using techniques like grid search
Many Mcq,s.Similar to cat exam
Ml case study . Eg loan default prediction
I applied via Campus Placement and was interviewed before Jul 2023. There were 3 interview rounds.
Medium General Aptitude questions and technical(Big Data, Python etc.)
Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.
Deep learning involves complex neural network architectures like CNNs and RNNs.
Machine learning algorithms include decision trees, SVM, k-means clustering, etc.
Understanding the math behind algorithms helps in optimizing model performance.
Equations like gradient descent, backpropagation, and loss functions are key concepts.
Practica...
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I applied via Campus Placement and was interviewed before Feb 2019. There were 3 interview rounds.
To choose optimum probability threshold from ROC, we need to balance between sensitivity and specificity.
Choose the threshold that maximizes the sum of sensitivity and specificity
Use Youden's J statistic to find the optimal threshold
Consider the cost of false positives and false negatives
Use cross-validation to evaluate the performance of different thresholds
To test time series trend break up, statistical tests like Augmented Dickey-Fuller test can be used.
Augmented Dickey-Fuller test can be used to check if a time series is stationary or not.
If the time series is not stationary, we can use differencing to make it stationary.
After differencing, we can again perform the Augmented Dickey-Fuller test to check for stationarity.
If there is a significant change in the mean or va...
Communicate transparently and offer alternative solutions.
Explain the limitations of the available data and the potential risks of making decisions based on incomplete information.
Offer alternative solutions that can be implemented with the available data.
Collaborate with the customer to identify additional data sources or explore other options to gather more data.
Provide regular updates on the progress of data collect...
I applied via Referral and was interviewed in May 2024. There was 1 interview round.
I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.
Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.
Preprocessed and cleaned the data to handle missing values and outliers.
Built and trained a machine learning model using algorithms like logistic regression and random forest.
Evaluated the model's perform...
I expect a supportive work environment, opportunities for growth, and clear communication.
Clear communication on project goals and expectations
Opportunities for professional development and growth
Supportive team environment
Regular feedback and performance evaluations
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