Decision

tree

Decision tree methodology

is a usually used data mining method for starting classification systems based

on multiple covariates or for developing forecast algorithms for a target

variable.

The basic concept of the

decision tree

1.

Nodes. There

are three types of nodes. (Lu and Song, 2017)

–

A root hub, additionally called a choice

hub, symbolizes a decision that will bring about the segment of all records

into at least two similarly selective subsets.

–

Internal hubs, additionally called shot

hubs, symbolize one of the conceivable choices accessible at that reality in

the tree structure, the upper edge of the hub is associated with its parent hub

and the most profound edge is associated with its kid hubs or leaf hubs.

–

Leaf hubs, likewise called end hubs, speak

to the last impact of a blend of choices or occasions.

2.

Branches. (Lu and Song, 2017)

–

Branches symbolize chance outcomes or events that originate from root

hubs and inward hubs.

–

A decision tree demonstrate is composed utilizing a pecking order of

branches. Every way from the root hub over inner hubs to a leaf hub speaks to a

grouping choice run the show.

–

These decision tree ways can likewise be spoken to as ‘assuming at that

point’ rules.

3. Splitting. (Lu and Song, 2017)

–

Only the input

variables interrelated to the target variable are charity to split parent nodes

into purer child nodes of the target variable.

–

Both separate input

variables and incessant input variables which are collapsed into two or more

categories can be used.

–

When building the

model one need first identify the most important input variables, and then

split records at the root node and at succeeding internal nodes into two or

more classes or ‘bins’ based on the status of these variables.

The type of the decision tree

·

Classification tree analysis is when the forecast

outcome is the class to which the data belongs.

·

Regression tree analysis is when the

predicted outcome can be considered a real number (e.g. the price of a house,

or a patient’s length of stay in a hospital).

Decision tree can quickly express complex options

plainly. Furthermore, can without much of a spring adjust a decision tree as

new data storms up noticeably available. Set up a decision tree to look at how shifting

information regards influence different choice options. Standard decision tree certification

is anything but difficult to receive. You can think about contending choices

even without finish data as far as threat and likely esteem. (Anon, 2017)

2. Logistic Regression

–

Logistic regression is used to find the

probability of event=Success and event=Failure. We should use logistic

regression when the dependent variable is binary (0/ 1, True/ False, Yes/ No)

in nature.

–

The binary

logistic model is charity to estimate the probability of a binary response

based on one or more predictor (or independent) variables (features).

–

It allows

one to say that the presence of a risk factor increases the odds of a given

outcome by a specific factor.

–

Logistic regression doesn’t require

linear relationship between dependent and independent variables. It can handle various types of relationships

because it applies a non-linear log transformation to the predicted odds ratio.

(Sachan,2017).

The type of logistic regression

1.

Binary

logistic regression (Wiley,2011)

–

used when the dependent variable is

dichotomous and the independent variables are either continuous or categorical.

–

When the

dependent variable is not dichotomous and is comprised of more than two

categories, a multinomial logistic regression.

2.

Multinomial

Logistic Regression (Wiley,2011)

–

The linear

regression analysis to conduct when the dependent variable is nominal with more

than two levels. Thus it is an extension of logistic regression, which

analyses dichotomous (binary) dependents.

–

Multinomial

regression is used to describe data and to explain the relationship between one

dependent nominal variable and one or more continuous-level (interval or ratio

scale) independent variables.

The logistic regression does not assume a linear relationship between

the independent variable and dependent variable and it may handle nonlinear

effects. The dependent variable need not be normally distributed. It does not

require that the independents be interval and unbounded. Logistic regression

come at a cost, it requires much more data to achieve stable, meaningful

results. logistic regression come at a cost: it requires much more data to

achieve stable, meaningful results. With standard regression, and dependent

variable, typically 20 data points per predictor is considered the lower bound.

For logistic regression, at least 50 data points per predictor is necessary to

achieve stable results (Wiley,2011)

3) Neural Network

Neural network is a method of the computing,

based on the interaction of multiple connected processing elements. Ability to

deal with incomplete information. When an element of the neural network fails,

it can continue without any problem by their parallel nature.

(Liu, Yang and Ramsay, 2011)

Basic concept of the

neural network (Liu, Yang and Ramsay, 2011)

1.

Computational Neuroscience

–

understanding and modelling operations of

single neurons or small neuronal circuits, e.g. minicolumns.

–

Modelling information processing in actual

brain systems, e.g. auditory tract.

–

Modelling human perception and cognition.

2.

Artificial Neural Networks

–

Used in Pattern recognition, adaptive

control, time series prediction and etc.

–

The

areas contributing to Artificial neural networks are Statistical Pattern

recognition, Computational Learning Theory, Computational Neuroscience,

Dynamical systems theory and Nonlinear optimisation.

The type of neural

network (Hinton,2010)

1. Feed-Forward

neural network

–

There is the commonest type of neural

network in practical application. The first layer is the input and the last

layer is output.

–

If the is more than one hidden layer, we

call them ‘deep’ neural networks. They compute a series of transformation that

change the similarities between cases.

2. Recurrent

networks

–

These have directed cycles in their

connection graph. That means you can sometimes get back to where you started by

following the arrows.

–

They can have complicated dynamic and this can

make them very difficult to train.

A neural network can perform tasks that a linear program cannot. A neural

network learns and does not need to be reprogrammed. It can be implemented in

any application. It can be implemented without any problem. Neural networks

requiring less formal statistical training, ability to implicitly detect

complex nonlinear relationships between dependent and independent