Q. In a case study involving predicting house prices, which feature would be most relevant?
A.
The color of the house
B.
The number of bedrooms
C.
The owner's name
D.
The year the house was built
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Solution
The number of bedrooms is a relevant feature that can significantly impact house prices in a predictive model.
Correct Answer:
B
— The number of bedrooms
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Q. In a regression problem, what does the R-squared value indicate?
A.
The strength of the relationship between variables
B.
The number of features used in the model
C.
The accuracy of the classification
D.
The error rate of the predictions
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Solution
R-squared indicates the strength of the relationship between the independent variables and the dependent variable in regression.
Correct Answer:
A
— The strength of the relationship between variables
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Q. In a regression problem, 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 few 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 instead of the underlying pattern, leading to poor performance on new data.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. In a supervised learning context, what is cross-validation used for?
A.
To increase the size of the training dataset
B.
To evaluate the model's performance on unseen data
C.
To reduce the dimensionality of the dataset
D.
To cluster the data points
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Solution
Cross-validation is used to evaluate the model's performance on unseen data by partitioning the dataset into training and validation sets.
Correct Answer:
B
— To evaluate the model's performance on unseen data
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Q. What does overfitting refer to in supervised learning?
A.
The model performs well on unseen data
B.
The model is too simple to capture the data patterns
C.
The model learns noise in the training data
D.
The model has high bias
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Solution
Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.
Correct Answer:
C
— The model learns noise in the training data
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Q. What does the term 'confusion matrix' refer to in classification tasks?
A.
A matrix that shows the relationship between features
B.
A table used to evaluate the performance of a classification model
C.
A method for dimensionality reduction
D.
A technique for data normalization
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Solution
A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.
Correct Answer:
B
— A table used to evaluate the performance of a classification model
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Q. What is a key characteristic of supervised learning?
A.
No labeled data is used
B.
It requires a training dataset with input-output pairs
C.
It is only applicable to classification tasks
D.
It does not involve any model training
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Solution
Supervised learning requires a training dataset with input-output pairs to learn the mapping from inputs to outputs.
Correct Answer:
B
— It requires a training dataset with input-output pairs
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Q. What is the purpose of cross-validation in supervised learning?
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
C.
To reduce the dimensionality of the dataset
D.
To improve the model's accuracy on the training set
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Solution
Cross-validation is used to evaluate the generalization ability of a model by partitioning the data into subsets and training/testing multiple times.
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 role of the loss function in supervised learning?
A.
To measure the accuracy of the model
B.
To quantify the difference between predicted and actual values
C.
To optimize the model's parameters
D.
To select features for the model
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Solution
The loss function quantifies the difference between predicted and actual values, guiding the optimization of the model during training.
Correct Answer:
B
— To quantify the difference between predicted and actual values
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Q. Which algorithm is typically used for binary classification?
A.
K-Means Clustering
B.
Linear Regression
C.
Logistic Regression
D.
Principal Component Analysis
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Solution
Logistic Regression is commonly used for binary classification tasks, predicting probabilities of class membership.
Correct Answer:
C
— Logistic Regression
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Q. Which of the following is a common evaluation metric for classification tasks?
A.
Mean Squared Error
B.
Accuracy
C.
R-squared
D.
Silhouette Score
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Solution
Accuracy is a common evaluation metric for classification tasks, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which of the following is an example of a regression problem?
A.
Classifying emails as spam or not spam
B.
Predicting house prices based on features
C.
Segmenting customers into groups
D.
Identifying objects in images
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Solution
Predicting house prices based on features is a regression problem, as it involves predicting a continuous value.
Correct Answer:
B
— Predicting house prices based on features
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Q. Which of the following is NOT a supervised learning algorithm?
A.
Support Vector Machines
B.
Decision Trees
C.
K-Means Clustering
D.
Random Forests
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Solution
K-Means Clustering is an unsupervised learning algorithm, while the others are supervised learning algorithms.
Correct Answer:
C
— K-Means Clustering
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Q. Which technique can help prevent overfitting in supervised learning?
A.
Increasing the number of features
B.
Using a more complex model
C.
Applying regularization
D.
Reducing the size of the training dataset
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Solution
Applying regularization techniques can help prevent overfitting by penalizing overly complex models.
Correct Answer:
C
— Applying regularization
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