Innovaccer Data Scientist Interview Questions and Answers

Q1. Basicn details to check for qualifications
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Q1. About my projects
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Q1. More details about ML models
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Q1. Probability related
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Q2. Stats related like IQR etc.
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Q1. Difference between entropy & information gain
Ans. 
Entropy measures randomness in data, while information gain measures the reduction in uncertainty after splitting data.
• Entropy is used in decision trees to measure impurity in a dataset before splitting it.

• Information gain is used in decision trees to measure the effectiveness of a split in reducing uncertainty.

• Entropy ranges from 0 (pure dataset) to 1 (completely impure dataset).

• Information gain is calculated as the d...read more
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Q2. LSTM & GRU, Which to use when ?
Ans. 
LSTM for longer sequences, GRU for faster training and less complex models.
• Use LSTM for tasks requiring long-term dependencies and memory retention.

• Use GRU for faster training and simpler models with fewer parameters.

• Consider using LSTM for tasks like language translation or speech recognition.

• Consider using GRU for tasks like sentiment analysis or text generation.
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Time Series data were given, we have to provide some insights

It contain both Aptitude and Coding about base models and Deep learning too

Q1. What different models technique ?
Ans. 
Different models techniques include linear regression, decision trees, random forests, support vector machines, and neural networks.
• Linear regression is used for predicting continuous values.

• Decision trees are used for classification and regression tasks.

• Random forests are an ensemble method based on decision trees.

• Support vector machines are used for classification tasks.

• Neural networks are used for complex pattern re
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Q2. What are performance metric where to use what?
Ans. 
Different performance metrics are used for different types of machine learning models to evaluate their effectiveness.
• For classification models, metrics like accuracy, precision, recall, F1 score, and ROC-AUC are commonly used.

• For regression models, metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared are commonly used.

• For clustering models, metrics like silhouette score and Davies-Bouldin in...read more
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Q1. Explain about Project
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Q2. What are problems faced in that project?
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Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
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Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
Ans. 
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
• Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.

• Adam optimizer uses adaptive learning rates for each parameter.

• Gradient Descent optimizer has a fixed learning rate for all parameters.

• Adam optimizer includes momentum to speed up convergence.

• Gradient Descent optimizer updates parameters b...read more
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Q3. When to use Relu and when not?
Ans. 
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
• ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.

• Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.

• Consider using Leaky ReLU or Sigmoid for output layers depending on the task.

• ReLU is computationally efficient and helps in preventing the vanishing gradient prob...read more
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Q1. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...read more
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Sql and python questions were there with basic logic check

Q1. Python code with funticion
Ans. 
Python code with function
• Define a function using 'def' keyword

• Include parameters inside parentheses

• Use 'return' statement to return a value from the function
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Q2. Sql with case statwment
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Q1. Questions related to the projects in the resume and some questions on machine learning concepts.
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Q1. Project related questions
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Q2. Theoretical ML questions
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Q1. Project related questions
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Q2. Theoretical ML questions
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