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Directions: Supervised learning involves training a model on labeled data.
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Q.1 What is the main objective of supervised learning in machine learning?
- To predict outcomes for new data
- To discover hidden patterns in data
- To reduce the dimensionality of data
- To generate new data from existing data
- None of the above
Directions: Classification tasks involve categorizing data into predefined classes.
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Q.2 Which algorithm is commonly used for classification tasks?
- K-Means Clustering
- Linear Regression
- Decision Trees
- Apriori Algorithm
- DBSCAN
Directions: Overfitting is a common problem when a model learns too much from the training data.
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Q.3 What does "overfitting" refer to in machine learning?
- A model performing well on both training and test data
- A model performing poorly on training data but well on test data
- A model performing well on training data but poorly on test data
- A model that generalizes well to unseen data
- A model that performs equally on all datasets
Directions: Unsupervised learning deals with unlabeled data to find patterns.
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Q.4 Which of the following is an example of unsupervised learning?
- Support Vector Machines
- Principal Component Analysis (PCA)
- Random Forest
- Naive Bayes
- Linear Discriminant Analysis
Directions: Learning rate is a key hyperparameter in training neural networks and other models.
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Q.5 What is the purpose of a "learning rate" in gradient descent optimization?
- To determine the size of steps taken towards the minimum of a function
- To increase the model's complexity
- To define the number of iterations
- To prevent the model from underfitting
- To ensure the model converges to a local maximum
Directions: Tokenization is a fundamental step in text preprocessing.
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Q.6 In natural language processing, what does "tokenization" refer to?
- Breaking down text into paragraphs
- Splitting text into sentences
- Breaking text into smaller units like words or phrases
- Removing punctuation and stopwords
- Translating text into another language
Directions: Evaluation metrics help measure the effectiveness of a model's predictions.
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Q.7 Which metric is commonly used to evaluate the performance of a classification model?
- Mean Squared Error (MSE)
- R-squared
- F1 Score
- Root Mean Squared Error (RMSE)
- Adjusted R-squared
Directions: CNNs are a type of deep learning model particularly effective in certain applications.
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Q.8 What is a "convolutional neural network" (CNN) primarily used for?
- Time series forecasting
- Text generation
- Image recognition
- Speech synthesis
- Reinforcement learning
Directions: An agent interacts with an environment to learn optimal actions.
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Q.9 In reinforcement learning, what is an "agent"?
- The environment where actions are taken
- A set of possible actions
- A reward function
- An entity that learns and makes decisions
- A policy for decision-making
Directions: Regularization techniques help in preventing overfitting.
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Q.10 What is "regularization" in machine learning?
- Increasing model complexity
- Adding noise to the data
- Penalizing complex models
- Optimizing hyperparameters
- Reducing model accuracy
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