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ElasticNet is a hybrid regularization technique that combines the L1 penalty of ________ and the L2 penalty of ________.

  • ElasticNet, Ridge
  • Lasso, Ridge
  • Ridge, Lasso
  • nan
ElasticNet combines the L1 penalty of Lasso and the L2 penalty of Ridge, providing a middle ground between the two techniques.
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How does the Root Mean Squared Error (RMSE) differ from Mean Squared Error (MSE)?

  • RMSE is half of MSE
  • RMSE is the square of MSE
  • RMSE is the square root of MSE
  • RMSE is the sum of MSE
The Root Mean Squared Error (RMSE) is the square root of the Mean Squared Error (MSE). While MSE measures the average squared differences, RMSE provides a value in the same unit as the original data. This makes RMSE more interpretable and commonly used when comparing model performance.
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What is classification in the context of Machine Learning?

  • Calculating numerical values
  • Finding relationships between variables
  • Grouping data into clusters
  • Predicting discrete categories
Classification is the process of predicting discrete categories or labels for given input data in machine learning. It divides the data into predefined classes or groups.
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The ________ measures the average of the squares of the errors, while the ________ takes the square root of that average in regression analysis.

  • MAE, MSE
  • MSE, RMSE
  • R-Squared, MAE
  • RMSE, MAE
The Mean Squared Error (MSE) calculates the average of the squared differences between predicted and actual values, and the Root Mean Squared Error (RMSE) takes the square root of that average. RMSE gives more weight to large errors and is more interpretable as it is in the same unit as the response variable.
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You are working with a large dataset, and you want to reduce its dimensionality using PCA. How would you decide the number of principal components to retain, considering the amount of variance explained?

  • By always retaining all principal components
  • By always selecting the first two components
  • By consulting with domain experts
  • By retaining components explaining at least a predetermined threshold of variance
The number of principal components to retain can be decided based on a predetermined threshold of variance explained. For example, you may choose to keep components that together explain at least 95% of the total variance.
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How is Recall defined in classification, and when is it an important metric to consider?

  • False Positives / Total predictions
  • True Negatives / (True Negatives + False Positives)
  • True Positives / (True Positives + False Negatives)
  • True Positives / (True Positives + False Positives)
Recall is the ratio of true positive predictions to the sum of true positives and false negatives. It measures the ability to correctly identify all relevant instances and is crucial when the cost of false negatives is high, such as in medical diagnoses.
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_________ regression adds a penalty equivalent to the square of the magnitude of coefficients, whereas _________ regression adds a penalty equivalent to the absolute value of the magnitude.

  • Lasso
  • Linear
  • Polynomial
  • Ridge
Ridge regression adds a penalty equivalent to the square of the magnitude of coefficients, whereas Lasso regression adds a penalty equivalent to the absolute value of the magnitude.
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In regression analysis, the metric that tells you the proportion of the variance in the dependent variable that is predictable from the independent variables is called _________.

  • Adjusted R-Squared
  • Mean Squared Error
  • R-Squared
  • Root Mean Squared Error
In regression analysis, R-Squared tells you the proportion of the variance in the dependent variable that is predictable from the independent variables. It provides a measure of how well the regression line fits the data.
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An educational institution wants to personalize its online learning platform for individual student needs. How would you leverage Machine Learning to achieve this goal?

  • Image Recognition, Fraud Detection
  • Personalized Learning Paths, Data Analysis
  • Recommender Systems, Drug Development
  • Supply Chain Management, Weather Prediction
Creating Personalized Learning Paths and analyzing student data using techniques like clustering or decision trees allows for the customization of content and resources according to individual student performance and preferences.
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You are working on a project where Simple Linear Regression seems appropriate, but the independent variable is categorical. How would you handle this situation?

  • Change the Dependent Variable
  • Ignore the Variable
  • Treat as Continuous Variable
  • Use Dummy Variables
For a categorical independent variable in Simple Linear Regression, you can create dummy variables to represent the categories.
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