Q. In the context of classification, what does ROC stand for?
A.
Receiver Operating Characteristic
B.
Receiver Output Curve
C.
Rate of Classification
D.
Random Output Curve
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Solution
ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.
Correct Answer:
A
— Receiver Operating Characteristic
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Q. In the context of regression, what does RMSE stand for?
A.
Root Mean Squared Error
B.
Relative Mean Squared Error
C.
Root Mean Squared Evaluation
D.
Relative Mean Squared Evaluation
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Solution
RMSE stands for Root Mean Squared Error, which measures the average magnitude of the errors between predicted and actual values.
Correct Answer:
A
— Root Mean Squared Error
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Q. What does a high precision but low recall indicate?
A.
The model is good at identifying positive cases but misses many
B.
The model is good at identifying all cases
C.
The model has a high number of false positives
D.
The model has a high number of false negatives
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Solution
High precision with low recall indicates that the model is good at identifying positive cases but misses many actual positives.
Correct Answer:
A
— The model is good at identifying positive cases but misses many
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Q. What does a high precision value indicate in a classification model?
A.
Most predicted positives are true positives
B.
Most actual positives are predicted correctly
C.
The model has a high recall
D.
The model is overfitting
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Solution
A high precision value indicates that most of the predicted positives are indeed true positives, reflecting the model's accuracy in positive predictions.
Correct Answer:
A
— Most predicted positives are true positives
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Q. What does the term 'confusion matrix' refer to?
A.
A matrix that shows the performance of a classification model
B.
A method for visualizing neural network layers
C.
A technique for data preprocessing
D.
A type of unsupervised learning algorithm
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Solution
A confusion matrix is a table that shows the performance of a classification model by comparing predicted and actual values.
Correct Answer:
A
— A matrix that shows the performance of a classification model
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Q. What does the term 'overfitting' refer to in machine learning?
A.
A model that performs well on training data but poorly on unseen data
B.
A model that generalizes well to new data
C.
A model that has high bias
D.
A model that is too simple
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Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to unseen data.
Correct Answer:
A
— A model that performs well on training data but poorly on unseen data
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Q. What is the main limitation of using accuracy as a performance metric?
A.
It does not consider false positives and false negatives
B.
It is not applicable to regression problems
C.
It is too complex to calculate
D.
It requires a large dataset
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Solution
The main limitation of accuracy is that it does not consider false positives and false negatives, which can be misleading in imbalanced datasets.
Correct Answer:
A
— It does not consider false positives and false negatives
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Q. What is the primary limitation of using accuracy as an evaluation metric?
A.
It is not applicable to binary classification
B.
It does not account for class imbalance
C.
It is difficult to calculate
D.
It only measures recall
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Solution
Accuracy does not account for class imbalance, which can lead to misleading interpretations of model performance.
Correct Answer:
B
— It does not account for class imbalance
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Q. What is the primary purpose of using cross-validation in model evaluation?
A.
To increase the training dataset size
B.
To reduce overfitting and ensure model generalization
C.
To improve model accuracy
D.
To select the best hyperparameters
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Solution
Cross-validation is used to reduce overfitting and ensure that the model generalizes well to unseen data.
Correct Answer:
B
— To reduce overfitting and ensure model generalization
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Q. What is the purpose of the confusion matrix?
A.
To visualize the performance of a classification model
B.
To calculate the accuracy of a regression model
C.
To determine feature importance
D.
To optimize hyperparameters
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Solution
The confusion matrix visualizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.
Correct Answer:
A
— To visualize the performance of a classification model
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Q. Which metric would you use to evaluate a model's performance in a multi-class classification problem?
A.
Binary Accuracy
B.
Macro F1 Score
C.
Mean Squared Error
D.
Logarithmic Loss
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Solution
Macro F1 Score is suitable for multi-class classification as it calculates the F1 score for each class and averages them.
Correct Answer:
B
— Macro F1 Score
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Q. Which metric would you use to evaluate a model's performance on a multi-class classification problem?
A.
Binary accuracy
B.
Macro F1 score
C.
Mean squared error
D.
Log loss
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Solution
The macro F1 score is suitable for multi-class classification as it calculates the F1 score for each class and averages them.
Correct Answer:
B
— Macro F1 score
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