A Practical Approach to Timeseries Forecasting Using Python - Model for Underfitting and Overfitting

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Information Technology (IT), Architecture
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What type of layer is added after the LSTM layer in the sequential model?
Dropout Layer
Dense Layer
Recurrent Layer
Convolutional Layer
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which optimizer is chosen for its fast computation and efficiency?
SGD
Adagrad
RMSprop
Adam
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the loss function used in the model compilation?
Huber Loss
Mean Squared Error
Hinge Loss
Cross-Entropy Loss
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How many epochs are set for training the model?
100
50
150
200
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is shuffling not required in this model training?
Shuffling is not supported by the framework
The data is already randomized
The model is simple
Shuffling increases computation time
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is plotted to monitor the model's performance?
Recall
Accuracy
Loss
Precision
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the two types of errors discussed in the context of model evaluation?
Overfitting and Underfitting
Precision and Recall
Bias and Variance
Sensitivity and Specificity
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