Info Edge Data Scientist Interview Questions and Answers

Q1. Concepts of Dropout during Test Time?
Ans. 
Dropout is a regularization technique used during training, but typically not applied during test time to ensure full model performance.
• Dropout randomly sets a fraction of input units to zero during training to prevent overfitting.

• During test time, dropout is usually turned off, allowing the full network to make predictions.

• This ensures that all neurons contribute to the output, providing a more accurate representation...read more
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Q2. How Random Forest Solves the Problem of Overfitting?
Ans. 
Random Forest mitigates overfitting by averaging multiple decision trees, enhancing generalization and robustness.
• Ensemble Learning: Combines predictions from multiple trees to reduce variance.

• Bootstrap Aggregating: Each tree is trained on a random subset of data, promoting diversity.

• Feature Randomness: Randomly selects features for splitting, preventing dominance of any single feature.

• Example: In a dataset with noise,...read more
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Q3. Suppose you have very high imbalance problem, how and which Model will you choose to report?
Ans. 
For high imbalance problems, choose models like Random Forest or XGBoost, and use techniques like SMOTE for better performance.
• Use ensemble methods like Random Forest or Gradient Boosting (e.g., XGBoost) for better handling of imbalanced data.

• Consider using resampling techniques such as SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset.

• Evaluate models using metrics like F1-score, precision, reca...read more
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Q4. Deep Concepts of Transformers?
Ans. 
Transformers are advanced neural network architectures that excel in processing sequential data, particularly in NLP tasks.
• Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sentence, e.g., in 'The cat sat on the mat', 'cat' and 'sat' are closely related.

• Positional Encoding: Since transformers don't have a built-in sense of order, positional encodings are added to input embedding...read more
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Q5. What is query, Key and Value pairs in self attention?
Ans. 
Query, Key, and Value are components of self-attention mechanisms in neural networks, enabling context-aware representations.
• In self-attention, each input element is transformed into three vectors: Query, Key, and Value.

• The Query vector represents the current element's focus, while the Key vector represents the context of other elements.

• The attention score is computed by taking the dot product of the Query and Key vect...read more
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Q6. How does Self Attention Works?
Ans. 
Self-attention allows models to weigh the importance of different words in a sequence when processing them, enhancing context understanding.
• Self-attention computes a weighted representation of input sequences, focusing on relevant parts.

• It uses three vectors: Query (Q), Key (K), and Value (V) to determine attention scores.

• For each word, the model calculates how much attention to pay to every other word in the sequence.

• ...read more
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Q7. Real Time Problems based on Multi Head Attention and it's use cases?
Ans. 
Multi-head attention enhances model performance by focusing on different parts of input data simultaneously.
• Improves natural language processing tasks like translation and summarization.

• Used in image processing for tasks like object detection and segmentation.

• Facilitates recommendation systems by analyzing user preferences from multiple perspectives.

• Enhances speech recognition by focusing on different phonetic features...read more
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Q8. Degree of Freedom, Chi Square Distribution?
Ans. 
Degrees of freedom in Chi-Square distribution indicate the number of independent values in a statistical calculation.
• Degrees of freedom (df) = number of categories - 1 in Chi-Square tests.

• Example: For a test with 5 categories, df = 5 - 1 = 4.

• In goodness-of-fit tests, df helps determine the critical value for hypothesis testing.

• Higher degrees of freedom lead to a more accurate approximation of the Chi-Square distributio...read more
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Q9. What is the mathematical framework behind Linear Regression? Real Time problem case.
Ans. 
Linear Regression uses statistical methods to model the relationship between variables, predicting outcomes based on input features.
• Linear regression assumes a linear relationship between the dependent and independent variables.

• The model can be represented as: Y = β0 + β1X1 + β2X2 + ... + βnXn + ε, where Y is the dependent variable.

• The coefficients (β) are estimated using the least squares method, minimizing the sum of...read more
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Q10. How are LLN and CLT are related?
Ans. 
LLN and CLT are statistical theorems that describe the behavior of sample averages as sample size increases.
• LLN (Law of Large Numbers) states that as the sample size increases, the sample mean converges to the population mean.

• CLT (Central Limit Theorem) states that the distribution of the sample mean approaches a normal distribution as sample size increases, regardless of the population's distribution.

• Both theorems are...read more
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Q11. Difference between Covariance and correlation?
Ans. 
Covariance measures the directional relationship between two variables, while correlation quantifies the strength and direction of that relationship.
• Covariance can take any value between -∞ and +∞, while correlation ranges from -1 to +1.

• Positive covariance indicates that two variables move in the same direction, while negative covariance indicates they move in opposite directions.

• Correlation standardizes covariance, ma...read more
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Q12. What are the assumptions underlying Linear Regression?
Ans. 
Linear regression relies on several key assumptions for valid results, including linearity, independence, and homoscedasticity.
• Linearity: The relationship between the independent and dependent variables should be linear. For example, predicting weight based on height.

• Independence: Observations should be independent of each other. For instance, data collected from different individuals should not influence each other.

• Ho...read more
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Q1. Explain all of decision tree and random forest?
Ans. 
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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Test 45 mins 30 ques

Q1. What is Linearregression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

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

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What is random forest
Ans. 
Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is robust to overfitting and noisy data, and it can handle large datasets...read more
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Q3. WHat is xgboost
Ans. 
XGBoost is an optimized distributed gradient boosting library designed for efficient and accurate large-scale machine learning.
• XGBoost stands for eXtreme Gradient Boosting.

• It is a popular machine learning algorithm known for its speed and performance.

• XGBoost is used for regression, classification, ranking, and user-defined prediction problems.

• It is based on the gradient boosting framework and uses decision trees as bas...read more
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Q1. Describe LSTM and GRU
Ans. 
LSTM and GRU are types of recurrent neural networks used for processing sequential data.
• LSTM (Long Short-Term Memory) networks are capable of learning long-term dependencies in data.

• GRU (Gated Recurrent Unit) networks are simpler than LSTM and have fewer parameters.

• LSTM has three gates (input, output, forget) while GRU has two gates (update, reset).

• LSTM is better at capturing long-term dependencies but is more complex,...read more
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Q2. Define Hypothesis Testing
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• It aims to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

• Common methods of hypothesis testing include t-tests, chi-square tests, and ANOVA.

• The p-value is used to dete...read more
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