Error inserting item 182. Total missing items: 1/882

Based on the provided documents, I'll explain the different types of machine learning
models:

1. Classification Models:
 Classification is performed by computers using statistical methods. Individual
observations are analyzed using a set of quantifiable properties called explanatory
variables or features.

Key aspects of classification:
-  Probabilistic classification is a common subclass that:
- Uses statistical inference to find the best class
- Outputs probabilities for each possible class
- Usually selects the class with highest probability
- Can output confidence values
- Can abstain when confidence is too low

2. Types of Classification:
 Classification can be:
- Binary classification: involves only two classes
- Multiclass classification: involves assigning an object to one of several classes
- Many classification methods are specifically designed for binary classification and
require combination for multiclass problems

3. Clustering Models:
 Two main methods in unsupervised learning are principal component and cluster
analysis. Clustering:
- Groups datasets with shared attributes
- Works with unlabeled, unclassified, and uncategorized data
- Identifies commonalities in data
- Reacts based on presence/absence of commonalities
- Helps detect anomalous data points

4. Unsupervised Learning Models:
 Unsupervised learning is where algorithms learn patterns exclusively from unlabeled
data, unlike supervised learning. There are also weak- or semi-supervision approaches
where a small portion of data is tagged.

5. Predictive Models:
 Predictive modeling:
- Uses statistics to predict outcomes
- Can be applied to future events or any unknown event
- Often used to detect crimes and identify suspects
- Often tries to guess probability of outcomes given input data

6. Deep Learning Models:
 Deep generative models (DGMs) combine generative models with deep neural networks.
They require:
- Increased scale of neural networks
- Increased scale of training data
Popular types include:
- Variational autoencoders (VAEs)
- Generative adversarial networks (GANs)
- Auto-regressive models

7. Language and Text Models:
 Recent trends show very large deep generative models:
- GPT-3 and GPT-2 are auto-regressive neural language models with billions of parameters
- BigGAN and VQ-VAE are used for image generation with hundreds of millions of parameters
- Jukebox is a large generative model for musical audio with billions of parameters

8. Model Categories Based on Statistical Approach:
 There are three broad categories:
1. Parametric models: Make specific assumptions about population parameters and underlying
distributions
2. Non-parametric models: Involve fewer structural assumptions but usually have strong
independence assumptions
3. Semi-parametric models: Include features of both parametric and non-parametric models

REFERENCES
__________

(Source 1) Statistical classification:
When classification is performed by a computer, statistical methods are normally used to
develop the algorithm. Often, the individual observations are analyzed into a set of
quantifiable properties, known variously as explanatory variables or features.

(Source 2) Statistical classification:
A common subclass of classification is probabilistic classification. Algorithms of this
nature use statistical inference to find the best class for a given instance. Unlike other
algorithms, which simply output a "best" class, probabilistic algorithms output a
probability of the instance being a member of each of the possible classes. The best class
is normally then selected as the one with the highest probability. However, such an
algorithm has numerous advantages over non-probabilistic classifiers: It can output a
confidence value associated with its choice (in general, a classifier that can do this is
known as a confidence-weighted classifier). Correspondingly, it can abstain when its
confidence of choosing any particular output is too low.

(Source 3) Statistical classification:
== Binary and multiclass classification == Classification can be thought of as two
separate problems – binary classification and multiclass classification. In binary
classification, a better understood task, only two classes are involved, whereas
multiclass classification involves assigning an object to one of several classes. Since
many classification methods have been developed specifically for binary classification,
multiclass classification often requires the combined use of multiple binary classifiers.

(Source 4) Unsupervised learning:
== Probabilistic methods == Two of the main methods used in unsupervised learning are
principal component and cluster analysis. Cluster analysis is used in unsupervised
learning to group, or segment, datasets with shared attributes in order to extrapolate
algorithmic relationships. Cluster analysis is a branch of machine learning that groups
the data that has not been labelled, classified or categorized. Instead of responding to
feedback, cluster analysis identifies commonalities in the data and reacts based on the
presence or absence of such commonalities in each new piece of data. This approach helps
detect anomalous data points that do not fit into either group.

(Source 5) Unsupervised learning:
Unsupervised learning is a framework in machine learning where, in contrast to supervised
learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in
the spectrum of supervisions include weak- or semi-supervision, where a small portion of
the data is tagged, and self-supervision.

(Source 6) Predictive modelling:
Predictive modelling uses statistics to predict outcomes. Most often the event one wants
to predict is in the future, but predictive modelling can be applied to any type of
unknown event, regardless of when it occurred. For example, predictive models are often
used to detect crimes and identify suspects, after the crime has taken place. In many
cases, the model is chosen on the basis of detection theory to try to guess the
probability of an outcome given a set amount of input data, for example given an email
determining how likely that it is spam.

(Source 7) Generative model:
== Deep generative models == With the rise of deep learning, a new family of methods,
called deep generative models (DGMs), is formed through the combination of generative
models and deep neural networks. An increase in the scale of the neural networks is
typically accompanied by an increase in the scale of the training data, both of which are
required for good performance. Popular DGMs include variational autoencoders (VAEs),
generative adversarial networks (GANs), and auto-regressive models.

(Source 8) Generative model:
Recently, there has been a trend to build very large deep generative models. For example,
GPT-3, and its precursor GPT-2, are auto-regressive neural language models that contain
billions of parameters, BigGAN and VQ-VAE which are used for image generation that can
have hundreds of millions of parameters, and Jukebox is a very large generative model for
musical audio that contains billions of parameters.

(Source 9) Predictive modelling:
Broadly speaking, there are two classes of predictive models: parametric and non-
parametric. A third class, semi-parametric models, includes features of both. Parametric
models make "specific assumptions with regard to one or more of the population parameters
that characterize the underlying distribution(s)". Non-parametric models "typically
involve fewer assumptions of structure and distributional form [than parametric models]
but usually contain strong assumptions about independencies".