Q. In the context of model selection, what does cross-validation help to achieve?
A.
Increase the training dataset size
B.
Reduce overfitting and assess model performance
C.
Select the best features
D.
Optimize hyperparameters
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Solution
Cross-validation helps to reduce overfitting and provides a better assessment of model performance by using different subsets of the data.
Correct Answer:
B
— Reduce overfitting and assess model performance
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Q. What is a common method for handling missing data in a dataset?
A.
Removing all rows with missing values
B.
Imputing missing values with the mean or median
C.
Ignoring the missing values
D.
All of the above
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Solution
All of the above methods can be used to handle missing data, depending on the context and the amount of missingness.
Correct Answer:
D
— All of the above
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Q. What is the primary goal of feature engineering in machine learning?
A.
To increase the size of the dataset
B.
To improve model performance by selecting relevant features
C.
To reduce the complexity of the model
D.
To visualize the data
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Solution
Feature engineering aims to improve model performance by selecting and transforming relevant features from the data.
Correct Answer:
B
— To improve model performance by selecting relevant features
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Q. What is the purpose of one-hot encoding in feature engineering?
A.
To normalize numerical features
B.
To convert categorical variables into a numerical format
C.
To reduce dimensionality
D.
To handle missing values
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Solution
One-hot encoding is used to convert categorical variables into a numerical format, allowing them to be used in machine learning algorithms.
Correct Answer:
B
— To convert categorical variables into a numerical format
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Q. What is the role of feature scaling in machine learning?
A.
To increase the number of features
B.
To ensure all features contribute equally to the model
C.
To reduce the size of the dataset
D.
To improve interpretability
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Solution
Feature scaling ensures that all features contribute equally to the model by standardizing or normalizing their values.
Correct Answer:
B
— To ensure all features contribute equally to the model
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Q. Which evaluation metric is most appropriate for a binary classification problem?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
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Solution
Accuracy is a common evaluation metric for binary classification problems, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which model selection technique helps to prevent overfitting by penalizing complex models?
A.
Grid Search
B.
Lasso Regression
C.
K-Fold Cross-Validation
D.
Random Search
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Solution
Lasso Regression includes a penalty term that discourages complexity in the model, helping to prevent overfitting.
Correct Answer:
B
— Lasso Regression
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Q. Which model selection technique involves comparing multiple models to find the best one?
A.
Grid Search
B.
Feature Scaling
C.
Data Augmentation
D.
Ensemble Learning
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Solution
Grid Search is a model selection technique that involves comparing multiple models and their hyperparameters to find the best performing one.
Correct Answer:
A
— Grid Search
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Q. Which of the following is a benefit of using ensemble methods in model selection?
A.
They always perform better than single models
B.
They reduce the variance of predictions
C.
They require less computational power
D.
They simplify the model interpretation
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Solution
Ensemble methods combine multiple models to reduce variance and improve prediction accuracy, often leading to better performance than individual models.
Correct Answer:
B
— They reduce the variance of predictions
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Q. Which of the following is a common technique for feature selection?
A.
Principal Component Analysis (PCA)
B.
K-Means Clustering
C.
Linear Regression
D.
Support Vector Machines
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Solution
Principal Component Analysis (PCA) is commonly used for feature selection by reducing dimensionality while retaining variance.
Correct Answer:
A
— Principal Component Analysis (PCA)
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Q. Which of the following is a common technique used in feature selection?
A.
Principal Component Analysis (PCA)
B.
K-Means Clustering
C.
Support Vector Machines (SVM)
D.
Random Forest Regression
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Solution
Principal Component Analysis (PCA) is a dimensionality reduction technique that can also be used for feature selection by identifying the most important features.
Correct Answer:
A
— Principal Component Analysis (PCA)
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Q. Which of the following is a real-world application of feature engineering?
A.
Image recognition
B.
Natural language processing
C.
Fraud detection
D.
All of the above
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Solution
Feature engineering is applicable in various real-world scenarios, including image recognition, natural language processing, and fraud detection.
Correct Answer:
D
— All of the above
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