Applied Machine Learning in Python Kevyn Collins Thompson week1 quiz answers These solutions are for reference only. It is recommended that you should solve the assignments amd quizes by yourself honestly then only it makes sense to complete the course. make sure you understand the solution answers in green colour ---------------------------------------------------------------------------------------------- 1。 Supervised Learning ---------------------------------------------------------------------------------------------- 2。 Supervised Learning ---------------------------------------------------------------------------------------------- 3。 Regression ---------------------------------------------------------------------------------------------- 4。 Feature Extraction ---------------------------------------------------------------------------------------------- 5。 • k=1: Class 2 • k=1: Class 1 • k=1: Class 0 • k=1: Class 0 ---------------------------------------------------------------------------------------------- 6。 A higher value of k leads to a more complex decision Partitions observations into k clusters where
each Memorizes the entire training set Given a data instance to classify, computes the ---------------------------------------------------------------------------------------------- 7。 See what type of cleaning or preprocessing still needs to It is not important ---------------------------------------------------------------------------------------------- 8。 To estimate how well the learned model will generalize To reduce the amount of labelled data needed for To reduce the number of features we need to consider To speed up the training process ---------------------------------------------------------------------------------------------- 9。 To avoid predictable splitting of the data To make experiments easily reproducible by always To avoid bias in data splitting To split the data into similar subsets
so that bias is not ---------------------------------------------------------------------------------------------- 10。 • X_train: (2500, ) • X_train: (10000, 28) • X_train: (2500, 50) • X_train: (7500, 50) • X_train: (10000, 50) What is the key purpose of splitting the dataset into training and test sets?The key purpose of splitting the dataset into training and test sets is: To estimate how well the learned model will generalize to new data.
What machine learning means?Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
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