Q. In classification problems, what does the term 'class label' refer to?
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A.
The input features of the data
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B.
The predicted output category
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C.
The algorithm used for training
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D.
The evaluation metric
Solution
The class label refers to the predicted output category in classification problems.
Correct Answer:
B
— The predicted output category
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Q. What is overfitting in the context of supervised learning?
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A.
The model performs well on training data but poorly on unseen data
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B.
The model is too simple to capture the underlying trend
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C.
The model has too few features
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D.
The model is trained on too little data
Solution
Overfitting occurs when a model performs well on training data but poorly on unseen data due to excessive complexity.
Correct Answer:
A
— The model performs well on training data but poorly on unseen data
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Q. What is the purpose of a confusion matrix in classification tasks?
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A.
To visualize the training process
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B.
To summarize the performance of a classification algorithm
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C.
To reduce overfitting
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D.
To optimize hyperparameters
Solution
A confusion matrix summarizes the performance of a classification algorithm by showing true vs. predicted classifications.
Correct Answer:
B
— To summarize the performance of a classification algorithm
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Q. What is the purpose of a loss function in supervised learning?
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A.
To measure the performance of the model
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B.
To optimize the model parameters
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C.
To define the model architecture
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D.
To preprocess the input data
Solution
The loss function measures the performance of the model by quantifying the difference between predicted and actual values.
Correct Answer:
A
— To measure the performance of the model
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Q. What type of supervised learning task is predicting house prices?
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A.
Classification
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B.
Clustering
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C.
Regression
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D.
Dimensionality Reduction
Solution
Predicting house prices is a regression task, as it involves predicting a continuous numerical value.
Correct Answer:
C
— Regression
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Q. What type of supervised learning task is used to predict categorical outcomes?
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A.
Regression
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B.
Classification
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C.
Clustering
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D.
Dimensionality Reduction
Solution
Classification is the type of supervised learning task used to predict categorical outcomes.
Correct Answer:
B
— Classification
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Q. Which algorithm is typically used for both regression and classification tasks?
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A.
K-Nearest Neighbors
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B.
Naive Bayes
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C.
Random Forest
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D.
Principal Component Analysis
Solution
Random Forest can be used for both regression and classification tasks, making it versatile.
Correct Answer:
C
— Random Forest
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Q. Which algorithm is typically used for multi-class classification problems?
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A.
Logistic Regression
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B.
K-Nearest Neighbors
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C.
Linear Regression
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D.
Principal Component Analysis
Solution
K-Nearest Neighbors can be used for multi-class classification by assigning the class based on the majority vote of neighbors.
Correct Answer:
B
— K-Nearest Neighbors
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Q. Which evaluation metric is commonly used for binary classification?
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A.
Mean Squared Error
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B.
Accuracy
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C.
Silhouette Score
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D.
R-squared
Solution
Accuracy is a common evaluation metric for binary classification, measuring the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which of the following is a common algorithm used for regression tasks?
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A.
K-Means
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B.
Linear Regression
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C.
Decision Trees
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D.
Support Vector Machines
Solution
Linear Regression is a common algorithm used for regression tasks, predicting a continuous output.
Correct Answer:
B
— Linear Regression
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Q. Which of the following is NOT a characteristic of supervised learning?
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A.
Requires labeled data
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B.
Can be used for both regression and classification
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C.
Learns from input-output pairs
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D.
Automatically discovers patterns without supervision
Solution
Supervised learning requires labeled data and does not automatically discover patterns without supervision.
Correct Answer:
D
— Automatically discovers patterns without supervision
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