Q. What is feature engineering?
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A.
The process of selecting the best model for a dataset
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B.
The process of creating new features from existing data
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C.
The method of evaluating model performance
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D.
The technique of tuning hyperparameters
Solution
Feature engineering involves creating new features from existing data to improve model performance.
Correct Answer:
B
— The process of creating new features from existing data
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Q. What is the main goal of dimensionality reduction?
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A.
To increase the number of features
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B.
To reduce the complexity of the model
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C.
To improve model interpretability and reduce overfitting
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D.
To enhance the training speed
Solution
Dimensionality reduction aims to simplify the model while maintaining its performance.
Correct Answer:
C
— To improve model interpretability and reduce overfitting
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Q. What is the main goal of using cross-validation in model selection?
-
A.
To increase the size of the training set
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B.
To reduce overfitting and assess model performance
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C.
To improve feature engineering
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D.
To select hyperparameters
Solution
Cross-validation helps reduce overfitting and provides a better assessment of model performance.
Correct Answer:
B
— To reduce overfitting and assess model performance
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Q. What is the purpose of cross-validation in model selection?
-
A.
To increase the size of the training dataset
-
B.
To assess how the results of a statistical analysis will generalize to an independent dataset
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C.
To reduce overfitting by simplifying the model
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D.
To improve the accuracy of the model
Solution
Cross-validation helps assess how well a model generalizes to an independent dataset.
Correct Answer:
B
— To assess how the results of a statistical analysis will generalize to an independent dataset
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Q. What is the purpose of model selection?
-
A.
To improve the accuracy of a single model
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B.
To choose the best model from a set of candidates
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C.
To reduce the dimensionality of the data
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D.
To increase the size of the dataset
Solution
Model selection aims to choose the best model from a set of candidates based on performance metrics.
Correct Answer:
B
— To choose the best model from a set of candidates
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Q. What is the purpose of using a validation set during model training?
-
A.
To train the model
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B.
To evaluate the model's performance during training
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C.
To test the model after training
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D.
To select features
Solution
A validation set is used to evaluate the model's performance during training and tune hyperparameters.
Correct Answer:
B
— To evaluate the model's performance during training
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Q. What is the purpose of using regularization in model selection?
-
A.
To increase model complexity
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B.
To prevent overfitting
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C.
To improve feature selection
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D.
To enhance data preprocessing
Solution
Regularization is used to prevent overfitting by adding a penalty for larger coefficients in the model.
Correct Answer:
B
— To prevent overfitting
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Q. Which evaluation metric is best for imbalanced classification problems?
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A.
Accuracy
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B.
F1 Score
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C.
Mean Squared Error
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D.
R-squared
Solution
F1 Score is better for imbalanced classification as it considers both precision and recall.
Correct Answer:
B
— F1 Score
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Q. Which of the following is a common method for encoding categorical variables?
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A.
Label Encoding
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B.
Min-Max Scaling
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C.
Standardization
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D.
Feature Extraction
Solution
Label Encoding is a common method for converting categorical variables into numerical format.
Correct Answer:
A
— Label Encoding
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Q. Which of the following is a common method for model selection?
-
A.
Grid Search
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B.
Data Augmentation
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C.
Feature Engineering
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D.
Ensemble Learning
Solution
Grid Search is a common method for systematically searching for the best model parameters.
Correct Answer:
A
— Grid Search
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Q. Which of the following is a method for feature scaling?
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A.
One-hot encoding
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B.
Min-Max scaling
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C.
Label encoding
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D.
Feature extraction
Solution
Min-Max scaling is a method used to scale features to a specific range, typically [0, 1].
Correct Answer:
B
— Min-Max scaling
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Q. Which of the following is a method for feature selection?
-
A.
K-means clustering
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B.
Recursive Feature Elimination
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C.
Gradient Descent
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D.
Support Vector Machines
Solution
Recursive Feature Elimination is a method used to select features by recursively removing the least important ones.
Correct Answer:
B
— Recursive Feature Elimination
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Q. Which of the following is NOT a common technique in feature engineering?
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A.
Normalization
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B.
One-hot encoding
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C.
Cross-validation
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D.
Polynomial features
Solution
Cross-validation is a model evaluation technique, not a feature engineering technique.
Correct Answer:
C
— Cross-validation
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Q. Which of the following is NOT a common technique in feature selection?
-
A.
Recursive Feature Elimination
-
B.
Principal Component Analysis
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C.
Random Forest Importance
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D.
Gradient Descent
Solution
Gradient Descent is an optimization algorithm, not a feature selection technique.
Correct Answer:
D
— Gradient Descent
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Q. Which technique can be used to handle missing data in a dataset?
-
A.
Feature scaling
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B.
Imputation
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C.
Normalization
-
D.
Regularization
Solution
Imputation is a technique used to fill in missing values in a dataset.
Correct Answer:
B
— Imputation
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Q. Which technique is used to handle missing values in a dataset?
-
A.
Feature scaling
-
B.
Imputation
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C.
Normalization
-
D.
Regularization
Solution
Imputation is the technique used to fill in missing values in a dataset.
Correct Answer:
B
— Imputation
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