2025 COMPREHESIVE CASE STUDY
FOR WEEK #7 16 YEARS OLD female
PATIENT WITH REASON: Peeing a lot
and it hurts class 6541 latest case .
walden university
Backpropagation through time (BPTT) - ANSWERS--A technique used to train recurrent
neural networks by computing gradients of the loss function for each weight at each
time step, updating the weights accordingly.
Biases - ANSWERS--Systematic errors or prejudices in data or algorithms that can lead
to unfair or inaccurate outcomes.
Confirmation bias - ANSWERS--A type of bias where the dataset favors a particular
viewpoint, potentially leading to a model that reinforces existing beliefs or assumptions.
Sampling bias - ANSWERS--A type of bias where the training dataset is not
representative of the entire population, potentially leading to a model that generalizes
poorly to unseen data.
Historical bias - ANSWERS--A type of bias where the training data does not reflect
changes over time, potentially leading to a model that is outdated or inaccurate in
predicting current trends.
Labelling bias - ANSWERS--A type of bias where the labels assigned to data are
subjective, inaccurate, or incomplete, potentially leading to a model that learns incorrect
or misleading patterns.
FOR WEEK #7 16 YEARS OLD female
PATIENT WITH REASON: Peeing a lot
and it hurts class 6541 latest case .
walden university
Backpropagation through time (BPTT) - ANSWERS--A technique used to train recurrent
neural networks by computing gradients of the loss function for each weight at each
time step, updating the weights accordingly.
Biases - ANSWERS--Systematic errors or prejudices in data or algorithms that can lead
to unfair or inaccurate outcomes.
Confirmation bias - ANSWERS--A type of bias where the dataset favors a particular
viewpoint, potentially leading to a model that reinforces existing beliefs or assumptions.
Sampling bias - ANSWERS--A type of bias where the training dataset is not
representative of the entire population, potentially leading to a model that generalizes
poorly to unseen data.
Historical bias - ANSWERS--A type of bias where the training data does not reflect
changes over time, potentially leading to a model that is outdated or inaccurate in
predicting current trends.
Labelling bias - ANSWERS--A type of bias where the labels assigned to data are
subjective, inaccurate, or incomplete, potentially leading to a model that learns incorrect
or misleading patterns.
