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Vedang Cellular Services Interview Questions and Answers

Updated 5 Feb 2024

Q1. Why do you think the objective of predictive modeling is minimizing the cost function? How would you define a cost function after all?

Ans.

The objective of predictive modeling is to minimize the cost function as it helps in optimizing the model's performance.

  • Predictive modeling aims to make accurate predictions by minimizing the cost function.

  • The cost function quantifies the discrepancy between predicted and actual values.

  • By minimizing the cost function, the model can improve its ability to make accurate predictions.

  • The cost function can be defined differently based on the problem at hand.

  • For example, in a binar...read more

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Q2. How can a string be reversed without affecting memory size?

Ans.

A string can be reversed without affecting memory size by swapping characters from both ends.

  • Iterate through half of the string length

  • Swap the characters at the corresponding positions from both ends

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Q3. What is the difference between XGBoost and AdaBoost algorithms?

Ans.

XGBoost and AdaBoost are both boosting algorithms, but XGBoost is an optimized version of AdaBoost.

  • XGBoost is an optimized version of AdaBoost that uses gradient boosting.

  • AdaBoost combines weak learners into a strong learner by adjusting weights.

  • XGBoost uses a more advanced regularization technique called 'gradient boosting'.

  • XGBoost is known for its speed and performance in large-scale machine learning tasks.

  • Both algorithms are used for classification and regression problems.

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Q4. What is the cost function for linear and logistic regression?

Ans.

The cost function for linear regression is mean squared error (MSE) and for logistic regression is log loss.

  • The cost function for linear regression is calculated by taking the average of the squared differences between the predicted and actual values.

  • The cost function for logistic regression is calculated using the logarithm of the predicted probabilities.

  • The goal of the cost function is to minimize the error between the predicted and actual values.

  • In linear regression, the c...read more

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Q5. Explain the concept of hypothesis testing intuitively using distribution curves for null and alternate hypotheses

Ans.

Hypothesis testing is a statistical method to determine if there is enough evidence to support or reject a claim.

  • Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

  • The null hypothesis assumes that there is no significant difference or relationship between variables.

  • The alternative hypothesis suggests that there is a significant difference or relationship between variables.

  • Distribution curves represent the probability distribution of data u...read more

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Q6. What is principal component analysis? When would you use it?

Ans.

Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space.

  • PCA is used to identify patterns and relationships in data by reducing the number of variables.

  • It helps in visualizing and interpreting complex data by representing it in a simpler form.

  • PCA is commonly used in fields like image processing, genetics, finance, and social sciences.

  • It can be used for feature extraction, noise reduction,...read more

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Q7. What would you do if the training data is skewed?

Ans.

Addressing skewed training data in data science

  • Analyze the extent of skewness in the data

  • Consider resampling techniques like oversampling or undersampling

  • Apply appropriate evaluation metrics that are robust to class imbalance

  • Explore ensemble methods like bagging or boosting

  • Use synthetic data generation techniques like SMOTE

  • Consider feature engineering to improve model performance

  • Regularize the model to avoid overfitting on the majority class

  • Collect more data to balance the cl...read more

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Q8. What is regularization? Why is it used?

Ans.

Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.

  • Regularization helps to reduce the complexity of a model by discouraging large parameter values.

  • It prevents overfitting by adding a penalty for complex models, encouraging simpler and more generalizable models.

  • Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net regularization.

  • Regularization can b...read more

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Q9. What is gradient boosting?

Ans.

Gradient boosting is a machine learning technique that combines multiple weak models to create a strong predictive model.

  • Gradient boosting is an ensemble method that iteratively adds new models to correct the errors made by previous models.

  • It is a type of boosting algorithm that focuses on reducing the residual errors in predictions.

  • Gradient boosting uses a loss function and gradient descent to optimize the model's performance.

  • Popular implementations of gradient boosting incl...read more

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