One Against All and One Against One Classification

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Questions and Answers

What is the primary goal of an agent in reinforcement learning?

To get the maximum positive rewards (correct)

To approximate the target function

To get the minimum negative rewards

To learn the mapping function from input to output

What is the goal of supervised learning?

To learn the mapping function from input to output (correct)

To minimize the negative rewards

To get the maximum positive rewards

To approximate the target function

What is a hypothesis in machine learning?

A candidate model that approximates a target function (correct)

A set of training examples

A set of all possible legal hypothesis

A mapping function from input to output

What is hypothesis space?

A set of all possible legal hypothesis Signup and view all the answers

What is version space?

A set of hypotheses that are consistent with the set of training examples Signup and view all the answers

What is a most general hypothesis?

A hypothesis that covers none of the negative examples Signup and view all the answers

What is the condition for a hypothesis to be consistent with the set of training examples?

h(x) = c(x) for all x ∈ D Signup and view all the answers

What is the relationship between a hypothesis h and a more general hypothesis h'?

h is more general than h' if h'(x) = 1 then h(x) = 1 Signup and view all the answers

What is the definition of a most general hypothesis G?

The largest axis-aligned rectangle that covers all positive examples and no negative examples. Signup and view all the answers

What is the main characteristic of a most specific hypothesis?

It covers none of the negative examples and there is no other hypothesis that covers no negative examples, such that the other hypothesis is more specific. Signup and view all the answers

What is the Version Space of hypotheses?

The set of all hypotheses that are consistent with the training set. Signup and view all the answers

What is the main purpose of the Vapnik-Chervonenkis dimension?

To guide the model selection process in machine learning. Signup and view all the answers

What is the issue that arises when a model is too complex and fits the noise in the training data?

Overfitting Signup and view all the answers

What is the main advantage of using a model with low VC dimension?

It is less prone to overfitting. Signup and view all the answers

What is the purpose of model selection in machine learning?

To select the best inductive bias for learning Signup and view all the answers

What happens when no hypothesis or multiple hypotheses are 1 for a given x?

The classifier rejects such cases Signup and view all the answers

What is the advantage of the one-against-one strategy in multi-class classification?

It allows for the creation of multiple classifiers for each pair of classes Signup and view all the answers

What is the purpose of inductive bias in machine learning?

To make assumptions to have a unique solution with the data Signup and view all the answers

What is the problem with a highly complex model?

It is prone to overfitting Signup and view all the answers

What is the goal of generalization in machine learning?

To perform well on unseen data Signup and view all the answers

What is the problem with an ill-posed problem in machine learning?

There is no unique solution Signup and view all the answers

What happens when the classifier assigns the same class to an instance in the one-against-one strategy?

The instance is assigned the majority class Signup and view all the answers

Study Notes

Classification Strategies

In the one-against-all strategy, we find K hypotheses (h1,..., hK) where only one of hi(x) is 1, and we assign the class Ci to x.
If multiple hi(x) is 1, we say that the classifier rejects such cases.

One-Against-One (OAO) Strategy

In OAO, a classifier is constructed for each pair of classes, resulting in K(K-1)/2 classifiers.
An unknown instance is classified with the class getting the most votes, with ties broken arbitrarily.

Model Selection and Generalization

Model selection refers to the process of picking a particular mathematical model from among different models.
Inductive bias is the set of assumptions we make to have learning possible, as learning is an ill-posed problem.
The primary goal of an agent in reinforcement learning is to improve performance by getting maximum positive rewards.

Supervised Learning

Supervised learning involves using an algorithm to learn the mapping function from input variables (x) to an output variable (Y).
The goal is to approximate the mapping function so well that the model can predict the output (y) for new input data (x).

Hypothesis and Hypothesis Space

A hypothesis in machine learning is a candidate model that approximates a target function for mapping inputs to output.
Hypothesis space is the set of all possible legal hypotheses.

Version Space

Version Space consists of all hypotheses that are consistent with the set of training examples.
A hypothesis is consistent with a set of training examples if it correctly classifies all the training examples.

Most General and Most Specific Hypothesis

A most general hypothesis covers none of the negative examples and is the most generic hypothesis.
A most specific hypothesis covers none of the negative examples and is the most specific hypothesis.

S, G, and the Version Space

Any hypothesis h ∈ H between S (most specific hypothesis) and G (most general hypothesis) is a valid hypothesis with no errors and thus consistent with the training set.
All such hypotheses h make up the Version Space of hypotheses.

Vapnik-Chervonenkis (VC) Dimension

The Vapnik-Chervonenkis dimension is a model capacity measurement used in statistics and machine learning.
It is an informal measure of a model’s capacity and is used to guide the model selection process.

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Description

Understand the concepts of one against all and one against one classification in machine learning, where a classifier is trained to distinguish one class from another.