LSTM_Module_6

What is the purpose of regression in machine learning?

• To classify images into categories

• To predict a continuous value based on input variables

• To detect objects in an image

• None of the above

Which of the following is the key assumption in linear regression?

• The relationship between variables is non-linear

• The dependent variable is categorical

• There is a linear relationship between the dependent and independent variables

• The data is not normally distributed

Which of the following is true about ARIMA (AutoRegressive Integrated Moving Average)?

• ARIMA is used for classifying objects in an image

• ARIMA requires stationary time series data

• ARIMA is only used for supervised learning tasks

• ARIMA is used to predict continuous variables but not time series data

What does the VAR (Vector Autoregressive) model help with in time series analysis?

• Predicting a single time series variable

• Capturing the relationships between multiple time series variables

• Performing classification tasks

• None of the above

Which of the following is a characteristic of LSTM (Long Short-Term Memory) networks?

• They are used for object detection

• They are designed to handle sequential data and learn long-term dependencies

• They are used for clustering tasks

• None of the above

What type of data is ARIMA most suitable for?

• Unstructured data like images or text

• Categorical data

• Time series data that shows trends or patterns over time

• None of the above

Which of the following is used to evaluate the performance of linear regression?

• Confusion matrix

• RMSE

• Precision and recall

• F1 score

In LSTM networks, what are the three key components that control the flow of information?

• Forget gate, input gate, and output gate

• Recurrent gate, input gate, and hidden gate

• Activation gate, input gate, and output gate

• Non input gate, and output gate and hidden gate

Which model is used for long-term forecasting of sequential data with long-term dependencies?

• Linear regression

• ARIMA

• LSTM

• VAR

Which of the following manufacturing applications would benefit most from using a regression model?

• Predicting the quality of a product based on machine parameters

• Identifying faulty parts on an assembly line

• Classifying types of products based on images

• Detecting workers not wearing safety gear