Q. In the context of evaluation metrics, what is a confusion matrix?
A.
A table used to describe the performance of a classification model
B.
A method to visualize the ROC curve
C.
A technique to calculate the AUC
D.
A way to measure the variance in predictions
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Solution
A confusion matrix summarizes the performance of a classification algorithm by showing the true positives, false positives, true negatives, and false negatives.
Correct Answer:
A
— A table used to describe the performance of a classification model
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Q. In the context of feature engineering, what does 'one-hot encoding' achieve?
A.
Reduces dimensionality
B.
Converts categorical variables into a numerical format
C.
Eliminates multicollinearity
D.
Increases the number of features exponentially
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Solution
One-hot encoding converts categorical variables into a numerical format by creating binary columns for each category, allowing algorithms to process them effectively.
Correct Answer:
B
— Converts categorical variables into a numerical format
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Q. In the context of feature scaling, what is the main purpose of normalization?
A.
To reduce the number of features
B.
To ensure all features contribute equally to the distance calculations
C.
To increase the variance of the dataset
D.
To eliminate outliers from the dataset
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Solution
Normalization scales features to a similar range, ensuring that all features contribute equally to distance calculations in algorithms like K-Means.
Correct Answer:
B
— To ensure all features contribute equally to the distance calculations
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Q. In the context of gaming, how are neural networks utilized?
A.
Game design
B.
Player behavior prediction
C.
Graphics rendering
D.
Sound design
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Solution
Neural networks can predict player behavior, allowing for more adaptive and engaging gaming experiences.
Correct Answer:
B
— Player behavior prediction
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Q. In the context of greedy algorithms, what does 'local optimum' refer to?
A.
The best solution overall
B.
The best solution in a local neighborhood
C.
The worst solution possible
D.
A solution that is not feasible
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Solution
A local optimum is the best solution within a local neighborhood, which may not lead to the global optimum.
Correct Answer:
B
— The best solution in a local neighborhood
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Q. In the context of IP addressing, what does CIDR stand for?
A.
Classless Inter-Domain Routing
B.
Classful Inter-Domain Routing
C.
Centralized Inter-Domain Routing
D.
Common Inter-Domain Routing
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Solution
CIDR stands for Classless Inter-Domain Routing, which allows for more efficient allocation of IP addresses.
Correct Answer:
A
— Classless Inter-Domain Routing
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Q. In the context of linear regression, what does 'heteroscedasticity' refer to?
A.
Constant variance of errors
B.
Non-constant variance of errors
C.
Independence of errors
D.
Normal distribution of errors
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Solution
Heteroscedasticity refers to the condition where the variance of the errors is not constant across all levels of the independent variable.
Correct Answer:
B
— Non-constant variance of errors
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Q. In the context of linear regression, what does 'overfitting' mean?
A.
The model performs well on training data but poorly on unseen data
B.
The model is too simple to capture the underlying trend
C.
The model has too few parameters
D.
The model is perfectly accurate
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Solution
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. In the context of linear regression, what does 'residual' refer to?
A.
The predicted value of the dependent variable
B.
The difference between the observed and predicted values
C.
The slope of the regression line
D.
The variance of the independent variable
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Solution
A residual is the difference between the observed value of the dependent variable and the value predicted by the regression model.
Correct Answer:
B
— The difference between the observed and predicted values
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Q. In the context of linear regression, what does the term 'homoscedasticity' refer to?
A.
Constant variance of the residuals
B.
Normal distribution of the errors
C.
Independence of observations
D.
Linearity of the relationship
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Solution
Homoscedasticity refers to the condition where the variance of the residuals is constant across all levels of the independent variable.
Correct Answer:
A
— Constant variance of the residuals
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Q. In the context of linear regression, what does the term 'overfitting' refer to?
A.
The model performs well on training data but poorly on unseen data
B.
The model is too simple to capture the underlying trend
C.
The model has too many features
D.
The model is perfectly accurate
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Solution
Overfitting occurs when a model learns the training data too well, capturing noise and leading to poor performance on unseen data.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. In the context of LL parsing, what does the '1' in LL(1) signify?
A.
One lookahead token is used.
B.
One leftmost derivation is produced.
C.
One parsing table is required.
D.
One recursive call is made.
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Solution
The '1' in LL(1) indicates that one lookahead token is used to make parsing decisions.
Correct Answer:
A
— One lookahead token is used.
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Q. In the context of model deployment, what does 'model drift' refer to?
A.
Changes in the model architecture
B.
Changes in the underlying data distribution
C.
Changes in the model's hyperparameters
D.
Changes in the deployment environment
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Solution
Model drift refers to changes in the underlying data distribution that can lead to a decrease in the model's performance over time.
Correct Answer:
B
— Changes in the underlying data distribution
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Q. In the context of model deployment, what does 'scalability' refer to?
A.
The ability to handle increased load
B.
The ability to reduce model size
C.
The ability to improve accuracy
D.
The ability to visualize data
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Solution
Scalability refers to the ability of the deployed model to handle increased load or demand.
Correct Answer:
A
— The ability to handle increased load
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Q. In the context of model evaluation, what does 'overfitting' refer to?
A.
Model performs well on training data but poorly on unseen data
B.
Model performs equally on training and test data
C.
Model is too simple to capture the underlying trend
D.
Model has high bias
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Solution
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.
Correct Answer:
A
— Model performs well on training data but poorly on unseen data
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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. In the context of model selection, what does cross-validation help to prevent?
A.
Overfitting
B.
Underfitting
C.
Data leakage
D.
Bias
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Solution
Cross-validation helps to prevent overfitting by ensuring that the model performs well on unseen data.
Correct Answer:
A
— Overfitting
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Q. In the context of neural networks, what does 'dropout' refer to?
A.
A method to reduce data size
B.
A technique to prevent overfitting
C.
A way to increase model complexity
D.
A process for feature selection
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Solution
Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of the neurons to zero during training.
Correct Answer:
B
— A technique to prevent overfitting
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Q. In the context of neural networks, what does 'epoch' refer to?
A.
A single pass through the training dataset
B.
The number of layers in the network
C.
The learning rate adjustment
D.
The size of the training batch
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Solution
An epoch refers to one complete pass through the entire training dataset during the training process.
Correct Answer:
A
— A single pass through the training dataset
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Q. In the context of neural networks, what does 'overfitting' mean?
A.
The model performs well on training data but poorly on unseen data
B.
The model is too simple to capture the underlying patterns
C.
The model has too few parameters
D.
The model is trained on too much data
Show solution
Solution
Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. In the context of neural networks, what is 'overfitting'?
A.
When the model performs well on training data but poorly on unseen data
B.
When the model has too few parameters
C.
When the model is too simple to capture the data patterns
D.
When the model converges too quickly
Show solution
Solution
Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on new data.
Correct Answer:
A
— When the model performs well on training data but poorly on unseen data
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Q. In the context of neural networks, what is 'transfer learning'?
A.
Training a model from scratch
B.
Using a pre-trained model on a new task
C.
Learning from unsupervised data
D.
Optimizing hyperparameters
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Solution
Transfer learning involves taking a pre-trained neural network and fine-tuning it for a different but related task.
Correct Answer:
B
— Using a pre-trained model on a new task
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Q. In the context of regression, what does R-squared indicate?
A.
The proportion of variance explained by the model
B.
The average error of predictions
C.
The correlation between predicted and actual values
D.
The number of features used in the model
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Solution
R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in the model.
Correct Answer:
A
— The proportion of variance explained by the model
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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. In the context of regression, which metric measures the average squared difference between predicted and actual values?
A.
F1 Score
B.
Mean Absolute Error
C.
Mean Squared Error
D.
Precision
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Solution
Mean Squared Error (MSE) measures the average of the squares of the errors, indicating the quality of a regression model.
Correct Answer:
C
— Mean Squared Error
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Q. In the context of supervised learning, what is a 'label'?
A.
The input feature of the model
B.
The output variable that the model is trying to predict
C.
The algorithm used for training
D.
The process of evaluating the model
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Solution
In supervised learning, a label is the output variable that the model aims to predict based on input features.
Correct Answer:
B
— The output variable that the model is trying to predict
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Q. In the context of supervised learning, what is the role of the target variable?
A.
It is the variable that is predicted by the model
B.
It is the variable used for feature engineering
C.
It is the variable that contains the input data
D.
It is the variable that determines the model architecture
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Solution
The target variable is the output variable that the model aims to predict based on the input features.
Correct Answer:
A
— It is the variable that is predicted by the model
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Q. In the context of SVM, what does 'margin' refer to?
A.
The distance between the closest data points of different classes
B.
The area under the ROC curve
C.
The number of support vectors used
D.
The total number of misclassified points
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Solution
The margin in SVM refers to the distance between the closest data points of different classes, which the algorithm aims to maximize.
Correct Answer:
A
— The distance between the closest data points of different classes
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Q. In the context of SVM, what does 'soft margin' refer to?
A.
A margin that allows some misclassifications
B.
A margin that is strictly enforced
C.
A margin that is not defined
D.
A margin that is only applicable to linear SVM
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Solution
A soft margin allows some misclassifications to achieve a better overall model performance, especially in cases of overlapping classes.
Correct Answer:
A
— A margin that allows some misclassifications
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Q. In the context of SVM, what does the term 'margin' refer to?
A.
The distance between the closest data points of different classes
B.
The area where no data points exist
C.
The total number of support vectors
D.
The error rate of the model
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Solution
The margin refers to the distance between the closest data points of different classes, which SVM aims to maximize.
Correct Answer:
A
— The distance between the closest data points of different classes
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