Module 5 - Machine Learning

Machine learning is _____.

Computers learning from experience with respect to some task and some performance measure

Basic machine learning approaches include ______ learning:

Supervised, Unsupervised, Semi-supervised, and Reinforcement

The proportion of samples correctly classified by a machine learning model are known is known as the model's _____.

accuracy

Machine learning techniques used when you do not know the groups in advance are known as ______ techniques.

clustering

In machine learning, a confusion matrix is used to _____.

determine the performance of the model

A column in a dataset which will be used as the basis for the categories identified by a machine learning model is known as a _____.

factor

A machine learning model which incorrectly identifies a patient who is actually healthy, as a diabetic, has returned a result known as _____.

false positive

Which of the following is an example of a true positive, in the testing of a machine learning model?

predicting that a tumor belongs to the group 'malignant', when that tumor in fact is malignant

The proportion of True Positives classified by a machine learning model is known as the model's _____.

sensitivity

Clustering methods in machine learning rely on measures of _____.

similarity or distance

The proportion of True Negatives obtained by a machine learning classifier is known as the model's _____.

specificity

In order to test the performance of a machine learning model, we need to _____ the data.

split

Classification techniques in machines learning used when you do know the groups in advance are known as _____ learning.

supervised

If you want to build a machine learning model which can correctly identify emails which contain span, by training it on emails which are already tagged as 'spam' or 'not spam', you should use _____.

supervised learning

In preparation for model-building, the dataset being used for a machine learning model is separated into two subsets, _____.

training and testing