Direction: 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?

1. A model performing well on both training and test data
2. A model performing poorly on training data but well on test data
3. A model performing well on training data but poorly on test data
4. A model that generalizes well to unseen data
5. A model that performs equally on all datasets

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A Quiz On Artificial Intelligence And Machine Learning

10 Questions 0 Attempts 157 Seen

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Direction: 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?

1. To predict outcomes for new data
2. To discover hidden patterns in data
3. To reduce the dimensionality of data
4. To generate new data from existing data
5. None of the above

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Direction: Classification tasks involve categorizing data into predefined classes.

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Q.2. Which algorithm is commonly used for classification tasks?

1. K-Means Clustering
2. Linear Regression
3. Decision Trees
4. Apriori Algorithm
5. DBSCAN

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Direction: 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?

1. A model performing well on both training and test data
2. A model performing poorly on training data but well on test data
3. A model performing well on training data but poorly on test data
4. A model that generalizes well to unseen data
5. A model that performs equally on all datasets

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Direction: 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?

1. Support Vector Machines
2. Principal Component Analysis (PCA)
3. Random Forest
4. Naive Bayes
5. Linear Discriminant Analysis

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Direction: 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?

1. To determine the size of steps taken towards the minimum of a function
2. To increase the model's complexity
3. To define the number of iterations
4. To prevent the model from underfitting
5. To ensure the model converges to a local maximum

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Direction: Tokenization is a fundamental step in text preprocessing.

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Q.6. In natural language processing, what does "tokenization" refer to?

1. Breaking down text into paragraphs
2. Splitting text into sentences
3. Breaking text into smaller units like words or phrases
4. Removing punctuation and stopwords
5. Translating text into another language

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Direction: 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?

1. Mean Squared Error (MSE)
2. R-squared
3. F1 Score
4. Root Mean Squared Error (RMSE)
5. Adjusted R-squared

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Direction: 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?

1. Time series forecasting
2. Text generation
3. Image recognition
4. Speech synthesis
5. Reinforcement learning

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Direction: An agent interacts with an environment to learn optimal actions.

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Q.9. In reinforcement learning, what is an "agent"?

1. The environment where actions are taken
2. A set of possible actions
3. A reward function
4. An entity that learns and makes decisions
5. A policy for decision-making

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Direction: Regularization techniques help in preventing overfitting.

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Q.10. What is "regularization" in machine learning?

1. Increasing model complexity
2. Adding noise to the data
3. Penalizing complex models
4. Optimizing hyperparameters
5. Reducing model accuracy

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