7 | Supervised machine learning Flashcards
What 3 components are part of any learning problem?
Task
The behavior or task that is being improved
Data
The experiences that are used to improve performance in the task, usually in the form of a sequence of examples
Measure of improvement
How the improvement is measured – for example, new skills that were not present initially, increasing accuracy in prediction, or improved speed
What issues does learning techniques face?
Task
Feedback
Representation
Online and offline
Measuring success
Bias
Learning as search
Noise
Interpolation and extrapolation
What is supervised learning?
One learning task is supervised learning, where there is a set of examples, and a set of features, partitioned into input features and target features. The aim is to predict the values of the target features from the input features.
What is a feature and a domain?
A feature is a function from examples into a value. If e is an example, and F is a feature, F(e) is the value of feature F for example e. The domain of a feature is the set of values it can return. Note that this is the range of the function, but is traditionally called the domain.
What is the learner (PC) given in a supervised learning task?
a set of input features, X1,…,Xn
a set of target features, Y1,…,Yk
a set of training examples, where the values for the input features and the target features are given for each example, and
a set of test examples, where only the values for the input features are given.
What are the two types of errors?
A false-positive error or type I error is a positive prediction that is wrong (i.e., the predicted value is true, and the actual value is false). A false-negative error or type II error is a negative prediction that is wrong (i.e., the predicted value is false, and the actual value is true).
What is a decision tree or a classification tree?
each internal (non-leaf) node is labeled with a condition, a Boolean function of examples
each internal node has two children, one labeled with true and the other with false
each leaf of the tree is labeled with a point estimate on the class.
What is linear regression?
Linear regression is the problem of fitting a linear function to a set of training examples, in which the input and target features are numeric.
What is gradient descent?
Gradient descent is an iterative method to find the minimum of a function. Gradient descent for minimizing error starts with an initial set of weights; in each step, it decreases each weight in proportion to its partial derivative. where η, the gradient descent step size, is called the learning rate. The learning rate, as well as the features and the data, is given as input to the learning algorithm. The partial derivative specifies how much a small change in the weight would change the error.
