Types of Machine Learning correct answers Supervised
- Goal: Prediction
Unsupervised
- Goal: Discovery
Reinforcement
Supervised Learning correct answers - Learns how to predict output from a given input
- two types of prediction
- classification
- discrete outputs
- regression
- continuous outputs
Unsupervised Learning correct answers - Find interesting patterns in data
- no training data
- not trying to predict any particular variable
- clustering: an unsupervised learning task that involves grouping data instances into
categories. Similar to classification but do not know classes ahead of time
semi-supervised learning correct answers - combines supervised and unsupervised
learning
- special case of supervised learning: you have a specific prediction task, but some of
your data has unknown outputs
reinforcement learning correct answers - setting:
- an agent interacts with some environment
- actions by the agent lead to different states in the environment
- some states provide rewards
- learning goal is to maximize reward
-used to learn models of how to behave
Features of a dataset correct answers - attributes/covariates
- input variables
- independent variables
- list of feature values for an instance is the feature vector
, label correct answers - value of the output variable
- dependent variable
- in classification, values that a label can take on are classes
Error/Loss of a prediction function correct answers - for classification, this is the
probability that the classifier outputs the correct label
- for regression, this is usually measured by how far away the predicted label will be
Error correct answers - True Error/Risk: a measure of how well a classifier will do on all
data it might encounter
- Usually, can only measure the error or loss on the training data called the
training/empirical error/risk
- Goal of machine learning is to learn a prediction function that minimizes true error
Generalization correct answers - Overfitting results when your function matched the
training data well but is not learning general rules that will work for new data
- Inductive Bias: restrictions on what a classifier can learn
General form of a line correct answers y = mx + b
- m and b are parameters/coefficients (constant once specified)
- x is the argument (input)
General Form of a Linear Function correct answers f(x1, x2, ..., xk) = (∑(over k) mi*xi) +
b
- one variable: line
- two variables: plane
- in general: hyperplane
K nearest neighbors correct answers - non linear?
- Classifies an instance as follows:
1. find the k labeled instances that have the lowest distance to the unlabeled instance
2. Return the majority class (most common label) in the set of k nearest instances
- can also be used for regression: replace "majority class" in step 2 with "average value"
- common variant of KNN: weigh the nearest neighbors by their distances
k in k nearest neighbors correct answers - if k is too small: prediction sensitive to noise
- if k is too large: algorithm loses local context that makes it work
K means clustering correct answers 1. Initialize cluster means
2. Repeat until assignments stop changing:
a) assign each instance to the cluster whose mean is nearest to the instance
b) update the cluster means based on new cluster assignments:
(1/|si|)(∑(xj in si) xj) where si is set of instances in cluster i and |si| is number of
instacnes in the cluster
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