Final answer:
The predict() function iterates over a list of prediction probabilities and uses a threshold of 0.5 to determine the binary outcome for each probability. If a probability is higher than the threshold, the prediction is 1; otherwise, it's 0.
Step-by-step explanation:
To write a function called predict() that can handle a list of prediction probabilities, one must understand how prediction probabilities generally work. In machine learning, prediction probabilities are the output of models that give the likelihood of a particular outcome. Probabilities are usually values between 0 and 1, with a higher value indicating more confidence in a prediction.
The predict() function would typically return a binary outcome based on a threshold, commonly 0.5 for binary classification tasks. If the prediction probability is above the threshold, the function could predict a '1' (positive class), and if it is below, a '0' (negative class). Here's what the function could look like in Python:
def predict(probabilities):
predictions = []
for prob in probabilities:
if prob >= 0.5:
predictions.append(1)
else:
predictions.append(0)
return predictions
This simple function iterates over the list of prediction probabilities and appends a 1 or 0 to a list called predictions based on the threshold comparison. Finally, it returns the list of predictions.