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Great Learning Senior Data Scientist Interview Questions and Answers

Updated 5 Feb 2024

Q1. how ensemble techniques works?

Ans.

Ensemble techniques combine multiple models to improve prediction accuracy.

  • Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.

  • Common ensemble techniques include bagging, boosting, and stacking.

  • Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.

  • Boosting involves iteratively training models on the data, with each sub...read more

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Q2. Difference between bias and variance

Ans.

Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.

  • Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.

  • Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.

  • High bias can cause an algorithm to miss relevant relations between features and target outputs...read more

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Q3. Types of ensemble techniques?

Ans.

Ensemble techniques combine multiple models to improve prediction accuracy.

  • Bagging: Bootstrap Aggregating

  • Boosting: AdaBoost, Gradient Boosting

  • Stacking: Meta-model combines predictions of base models

  • Voting: Combining predictions of multiple models by majority voting

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Q4. Classification techniques?

Ans.

Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.

  • Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.

  • Classification can be binary (two classes) or multi-class (more than two classes).

  • Evaluation metrics for classification include accuracy, precision, recall, and F1 score.

  • Feature selection and engineering can improve classifica...read more

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Q5. Explain about random forest

Ans.

Random forest is an ensemble learning method for classification, regression and other tasks.

  • Random forest builds multiple decision trees and combines their predictions to improve accuracy.

  • It uses bagging technique to create multiple subsets of data and features for each tree.

  • Random forest reduces overfitting and is robust to outliers and missing values.

  • It can handle high-dimensional data and is easy to interpret feature importance.

  • Example: predicting customer churn, fraud det...read more

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Q6. Explain bosting?

Ans.

Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.

  • Boosting iteratively trains weak models on different subsets of data

  • Each subsequent model focuses on the misclassified data points of the previous model

  • Final prediction is made by weighted combination of all models

  • Examples include AdaBoost, Gradient Boosting, XGBoost

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Q7. explain bagging

Ans.

Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.

  • Bagging stands for Bootstrap Aggregating.

  • It involves creating multiple subsets of the original dataset by randomly sampling with replacement.

  • Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.

  • Bagging reduces overfitting and variance in the model.

  • Random Forest is an example of...read more

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