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What does 'data imputation' refer to in data preprocessing?
Response:
A. Removing irrelevant features from the dataset
B. Filling in missing or null values in the data
C. Transforming categorical data into numerical data
D. Scaling data to a standard range
Answer: B
NEW QUESTION # 28
In machine learning, what is 'bias-variance tradeoff'?
Response:
A. The compromise between the model's ability to generalize and its accuracy on training data
B. The balance between the number of features and the size of the dataset
C. The tradeoff between the learning rate and the number of epochs
D. The relationship between the model's complexity and its performance on unseen data
Answer: A
NEW QUESTION # 29
You are using a CNN to classify images in a dataset. After several epochs of training, you notice that the model performs well on the training set but poorly on the validation set. This suggests overfitting.
What steps should you take to improve the generalization of the model?
Response:
A. Reduce the size of the dataset and increase the number of convolutional layers
B. Remove padding to simplify the architecture
C. Train the model for more epochs and reduce the use of regularization
D. Implement dropout in the fully connected layers, apply data augmentation to increase the variability of the training data, and consider early stopping to prevent overfitting
Answer: D
NEW QUESTION # 30
Which functions are typically used for data manipulation in the Pandas library?
(Choose two)
Response:
A. plt.show()
B. tf.keras.models()
C. pd.merge()
D. pd.DataFrame()
Answer: C,D
NEW QUESTION # 31
What is the primary benefit of using the 'early stopping' technique in training machine learning models?
Response:
A. To speed up the training process by reducing the number of iterations
B. To prevent overfitting by stopping the training when performance on a validation set starts to degrade
C. To optimize the hyperparameters automatically during training
D. To increase the accuracy of the model on the training data
Answer: B
NEW QUESTION # 32
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