UNIT – III - CHAPTER – II
SUPERVISED LEARNING : CLASSIFICATION
In supervised learning, the labelled training data provide the basis
for learning. According to the definition of machine learning, this
labelled training data is the experience or prior knowledge
Some more examples of supervised learning are as follows:
Prediction of results of a game based on the past analysis of
results
Predicting whether a tumor is malignant or benign on the basis of
the analysis of data
Price prediction in domains such as real estate, stocks, etc.
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Training data is the past information with known value of class field or
‘label’. Hence, we say that the ‘training data is labelled’ in the case of
supervised learning (refer Fig.). Contrary to this, there is no labelled
training data for unsupervised learning. Semi-supervised learning, as
depicted in Figure, uses a small amount of labelled data along with
unlabelled data for training.
2
CLASSIFICATION MODEL
 When we are trying to predict a categorical or nominal variable,
the problem is known as a classification problem. A classification
problem is one where the output variable is a category such as
‘red’ or ‘blue’ or ‘malignant tumor’ or ‘benign tumor’, etc.
 Whereas when we are trying to predict a numerical variable such
as ‘price’, ‘weight’, etc. the problem falls under the category of
regression.
Supervised machine learning is as good as the data used to train it.
If the training data is poor in quality, the prediction will also be far
from being precise.
3
The following fig depicts the typical process of classification
where
a
classification
model
is
obtained
from
the
labelled training data by a classifier algorithm. On the
basis of the model, a class label (e.g. ‘Intel’ as in the case
of the test data referred in Fig.) is assigned to the test
data.
FIG. Classification model
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 classification is a type of supervised learning where a target feature,
which is of categorical type, is predicted for test data on the basis of
the information imparted by the training data.
The target categorical feature is known as class .
Some typical classification problems include the following:
Image classification
Disease prediction
Win–loss prediction of games
Prediction of natural calamity such as earthquake, flood, etc.
Handwriting recognition
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CLASSIFICATION LEARNING STEPS
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CLASSIFICATION LEARNING STEPS
• The
Problem Identification
problem needs to be a well-formed problem, i.e. a
problem with well-defined goals and benefit, which has a
long-term impact.
• The required data set that represents the problem needs to
Identification of
Required Data
be identified/evaluated.
• For example: If the problem is to predict whether a tumor is
malignant or benign, then the corresponding patient data sets
related to tumors are to be identified
• the data is gathered - raw format and is not ready for immediate
Data Preprocessing
analysis.
• All the unnecessary/irrelevant data elements are removed.
• It ensures, the data is ready to be fed into the ML algorithm.
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CLASSIFICATION LEARNING STEPS
•the user should decide what kind of data set is to be used as a
Definition of
Training Data Set
training set. A set of data input (X) and corresponding outputs (Y) is
gathered either from human experts or experiments.
•For Ex: signature analysis, the training data set might be a single
handwritten alphabet, a handwritten word or an entire line.
• involves determining the structure of the learning function
Algorithm Selection
and the corresponding learning algorithm. On the basis of
various parameters, the best algorithm for a given problem is
chosen.
Training
• The learning algorithm identified in the previous step is run on
the training set for further fine tuning.
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CLASSIFICATION LEARNING STEPS
Evaluation with the
Test Data Set
• Training data is run on the algorithm, and its
performance is measured here. If a suitable
result is not obtained, further training of
parameters may be required
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COMMON CLASSIFICATION ALGORITHMS
Following are the most common classification algorithms
1. k-Nearest Neighbour(k-NN)
2. Decision tree
3. Random forest
4. Support Vector Machine (SVM)
5. Naïve Bayes classifier
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k - Nearest Neighbour (k-NN)
 The unknown and unlabelled data which comes for a
prediction problem is judged on the basis of the
training data set elements which are similar to the
unknown element.
 So, the class label of the unknown element is
assigned on the basis of the class labels of the
similar
training
data
set
elements
(
can
be
considered as neighbours of the unknown element).
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k - Nearest Neighbour (k-NN)
Input: Training data set, test data set (or data points), value of ‘k’ (i.e.
number of nearest neighbours to be considered)
Steps:
Do for all test data points
Calculate the distance (usually Euclidean distance) of the test data
point from the different training data points.
Find the closest ‘k’ training data points, i.e. training data points
whose distances are least from the test data point.
If k = 1
Then assign class label of the training data point to the test data point
Else
Whichever class label is predominantly present in the training data
points, assign that class label to the test data point
End do
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 Let us consider a very simple Student data
set as depicted in Figure.
 It consists of 15 students studying in a class.
 Each of the students has been assigned a
score on a scale of 10 on two performance
parameters – ‘Aptitude’ and ‘Communication’.
 Also, a class value is assigned to each
student based on the following criteria:
1. Students having good communication skills
as well as a good level of aptitude have
been classified as ‘Leader’
2. Students having good communication skills
but not so good level of aptitude have
been classified as ‘Speaker’
3. Students having not so good
communication skill but a good level of
aptitude have been classified as ‘Intel’
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
while building a classification model, a
part of the labelled input data is retained
as test data.

The remaining portion of the input data is
used to train the model – hence known as
training data.

The motivation to retain a part of the data
as
test
data
is
to
evaluate
the
performance of the model.

The performance of the classification
model is measured by the number of
correct classifications made by the model
when applied to an unknown data set.

As depicted in Figure, the record of the
student named Josh is assumed to be the
test data.

Now that we have the training data and
test data identified, we can start with the
modelling
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In the kNN algorithm, the class label of
Considering a very simple data set
the test data elements is decided by the
having two features (say f1 and f2),
class label of the training data elements
Euclidean distance between two data
which are neighbouring, i.e. similar in
elements d1 and d2 can be measured by
nature. But there are two challenges:
1. What is the basis of this similarity or
•
for data element d1
when can we say that two data elements
•
are similar?
•
each test data element?
are many measures of similarity, the most
measure
similarity
f21 = value of feature f2 for data
element d1
To answer the first question, though there
common approach adopted
f12 = value of feature f1 for data
element d2
2. How many similar elements should be
considered for deciding the class label of
where f11 = value of feature f1
by kNN
between
two
to
•
f22 = value of feature f2 for data
element d2
data
elements is Euclidean distance.
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The training data points of the Student data set considering only the features
‘Aptitude’ and ‘Communication’ can be represented as dots in a two-dimensional
feature space.
The reason for considering two-dimensional data space is that we are considering
just the two features of the Student data set, i.e. ‘Aptitude’ and ‘Communication’,
for doing the classification.
The feature ‘Name’ is ignored because, as we can understand, it has no role to
play in deciding the class value.
The test data point for student Josh is represented as an asterisk in the same
space.
To find out the closest or nearest neighbours of the test data point, Euclidean
distance of the different dots need to be calculated from the asterisk.
Then, the class value of the closest neighbours helps in assigning the class value of
the test data element.
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The second question is The answer lies in the value of ‘k’ which is a user-defined parameter
given as an input to the algorithm.
In the k-NN algorithm, the value of ‘k’ indicates the number of
neighbors that need to be considered.
For example, if the value of k is 3, only three nearest neighbours or
three training data elements closest to the test data element are
considered. Out of the three data elements, the class which is under
majority voting is considered as the class label to be assigned to the
test data.
In case, the value of k is 1, only the closest training data element
is considered. The class label of that data element is directly assigned
to the test data element.
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 when the value of k is taken as 1, only one
training data point needs to be considered.
The training record for student Gouri comes
as the closest one to test record of Josh,
with a distance value of 1.118. Gouri has
class value ‘Intel’. So, the test data point is
also assigned a class label value ‘Intel’.
 If the value of k is taken as 3, the closest
neighbours of Josh in the training data set
are Gouri, Susant, and Bobby with distances
being 1.118, 1.414, and 1.5, respectively.
 Gouri and Bobby have class value ‘Intel’, while
Susant has class value ‘Leader’.
 In this case, the class value of Josh is
decided by majority voting. Because the class
value of ‘Intel’ is formed by the majority of
the neighbours, the class value of Josh is
assigned as ‘Intel’.
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It is difficult to decide the value of k. The reasons are as follows:
 If the value of k is very large , the class label of the majority class of
the training data set will be assigned to the test data regardless of the
class labels of the neighbors nearest to the test data.
 If the value of k is very small , the class value of a noisy data or outlier in
the training data set which is the nearest neighbor to the test data will be
assigned to the test data.
 The best k value is somewhere between these two extremes.
One common practice among few strategies is to set k equal to the
square root of the number of training records.
An alternative approach is to test several k values on a variety of
test data sets and choose the one that delivers the best performance.
Another interesting approach is to choose a larger value of k, but
apply a weighted voting process in which the vote of close neighbors is
considered more influential than the vote of distant neighbors.
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Why the k-NN algorithm is called a lazy learner?
K-NN algorithm,
stores the training data and directly applies the philosophy of nearest
neighborhood finding to arrive at the classification when the problem comes. So, for k-NN,
there is no learning happening in the real sense. Therefore, k-NN falls under the category of
lazy learner.
Strengths of the k-NN algorithm
1.
Extremely simple algorithm – easy to understand
2. Very effective in certain situations, e.g. for recommender system design
3. Very fast or almost no time required for the training phase
Weaknesses of the k-NN algorithm
1. Does not learn anything in the real sense. Classification is done completely on the basis of
the training data. So, it has a heavy reliance on the training data.
2. Because there is no model trained in real sense and the classification is done completely on
the basis of the training data, the classification process is very slow.
3. Also, a large amount of computational space is required to load the training data for
classification.
Applications of the k-NN algorithm : Recommender system, searching documents/ contents
similar to a given document/content. This is an area under information retrieval and is known
as concept search.
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Decision tree
•
Decision tree learning is one of the most widely adopted algorithms for
classification.
•
As the name indicates, it builds a model in the
structure.
•
A decision tree is used for multi-dimensional analysis with multiple
classes.
•
It
is
characterized
by
fast
execution
time
and
ease
in
the
interpretation of the rules.
•
The goal of decision tree learning is to create a model (based on the
past data called past vector) that predicts the value of the output
variable based on the input variables in the feature vector.
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 Each
node
(or
decision
node)
of
a
decision
tree
corresponds to one of the feature vector.
 From every node, there are edges to children, wherein
there is an edge for each of the possible values (or range
of values) of the feature associated with the node.
 The tree terminates at different leaf nodes (or terminal
nodes) where each leaf node represents a possible value
for the output variable.
 The output variable is determined by following a path
that starts at the root and is guided by the values of
the input variables.
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 A decision tree is usually represented in the format depicted in Figure.
 Each internal node (represented by boxes) tests an attribute (represented
as ‘A’/‘B’ within the boxes).
 Each branch corresponds to an attribute value (T/F) in the above case.
 Each leaf node assigns a classification.
 The first node is called as ‘Root’ Node.
 Branches from the root node are called as ‘Branch’ Nodes where ‘A’ is the
Root Node (first node). ‘B’ is the Branch Node.
 ‘T’ & ‘F’ are Leaf Nodes.
Thus, a decision tree consists of three types of
nodes:
 Root Node
 Branch Node
 Leaf Node
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Building a decision tree
 Decision trees are built corresponding to the training data
following an approach called recursive partitioning.
 The approach splits the data into
on the basis
of the feature values.
 It starts from the root node, which is nothing but the entire
data set.
 It first selects the feature which predicts the target class.
 with data in each partition having a distinct value for the
feature based on which the partitioning has happened.
 This is the first set of branches.
 Likewise, the algorithm continues splitting the nodes on the
basis of the feature which helps in the best partition.
 This continues till a stopping criterion is reached. The usual
stopping criteria are –
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1. All or most of the examples at a particular node have
the same class
2. All features have been used up in the partitioning
3. The tree has grown to a pre-defined threshold limit
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27
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According to the table, job offer condition (i.e. the outcome) is FALSE for all
the cases where Aptitude = Low, irrespective of other conditions.
So, the feature Aptitude can be taken up as the first node of the decision tree.
For Aptitude = High, job offer condition is TRUE for all the cases where
Communication = Good.
For cases where Communication = Bad, job offer condition is TRUE for all the
cases where CGPA = High.
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Searching a decision tree
By using the above decision tree depicted in Figure, we need to predict whether
Chandra might get a job offer for the given parameter values: CGPA = High,
Communication = Bad, Aptitude = High, Programming skills = Bad.
There are multiple ways to search through the trained decision tree for a solution
to the given prediction problem.
Exhaustive search
1. Place the item in the first group (class). Recursively examine solutions with the
item in the first group (class).
2. Place the item in the second group (class). Recursively examine solutions with the
item in the second group (class).
3. Repeat the above steps until the solution is reached.
Exhaustive search travels through the decision tree exhaustively, but it
will take much time when the decision tree is big with multiple leaves and multiple
attribute values.
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To overcome the problem in Exhaustive search, the following technique
can be adopted:
Branch and bound search
 It uses an existing best solution to sidestep searching of the entire
decision tree in full.
 When the algorithm starts, the best solution is well defined to have the
worst possible value; thus, any solution it finds out is an improvement.
 This makes the algorithm initially run down to the left-most branch of
the tree, even though that is unlikely to produce a realistic result.
 In the partitioning problem, that solution corresponds to putting every
item in one group, and it is an unacceptable solution.
 A program can speed up the process by using a fast experiment to find an
initial solution. This can be used as an input for branch and bound.
 If the heuristic is right, the savings can be substantial.
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Decision tree based on the training data (depicting a sample path)
Fig. depicts a sample path (thick line) for the conditions CGPA = High,
Communication = Bad, Aptitude = High and Programming skills = Bad.
According to the above decision tree, the prediction can be made as
Chandra will get the job offer.
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There are many implementations of decision tree, the most
prominent ones being

C5.0,

CART (Classification and Regression Tree),

CHAID (Chi-square Automatic Interaction Detector) and

ID3 (Iterative Dichotomiser 3) algorithms.
The biggest challenge of a decision tree algorithm is to find out
which feature to split upon.
The main driver for identifying the feature is that the data
should be split in such a way that the partitions created by the
split should contain examples belonging to a single class. If that
happens, the partitions are considered to be pure.
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 Entropy is a measure of impurity of an attribute or feature adopted by
many algorithms such as ID3 and C5.0.
 The information gain is calculated on the
after a data set is split according to a particular attribute
(A).
 Constructing a decision tree is all about finding an attribute that
returns the highest information gain (i.e. the most homogeneous
branches).
Entropy of a decision tree
 Let us say S is the sample set of training examples.
 Then, Entropy (S) measuring the impurity of S is defined as
where c is the number of different class labels and
p refers to the proportion of values falling into the i-th class
label.
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 For example, with respect to the training data in Figure, we have two
values for the target class ‘Job Offered?’ – Yes and No.
 The value of p for class value ‘Yes’ is 0.44 (i.e. 8/18) and that
 for class value ‘No’ is 0.56 (i.e. 10/18). So, we can calculate the entropy
as
Entropy(S) = -0.44 log (0.44) - 0.56 log (0.56) = 0.99.
Information gain of a decision tree
Information gain for a particular feature A is calculated by the difference in
entropy before a split (or Sbs ) with the entropy after the split (Sas).
Information Gain (S, A) = Entropy (Sbs ) − Entropy (Sas)
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Random Forest Algorithm
 It is a popular machine learning algorithm that belongs to the supervised learning
technique.
 It can be used for both Classification and Regression problems in ML.
 It is based on the concept of ensemble learning, which is a process
of combining multiple classifiers to solve a complex problem and to improve the
performance of the model.
As the name suggests, "Random Forest is a classifier that contains a number of
decision trees on various subsets of the given dataset and takes the average
to improve the predictive accuracy of that dataset."
Instead of relying on one decision tree, the random forest takes the prediction
from each tree and based on the majority votes of predictions, and it predicts the
final output.
The greater number of trees in the forest leads to higher accuracy
and prevents the problem of overfitting.
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For example: consider the fruit basket as the data as shown
in the figure below. Now n number of samples are taken from
the fruit basket, and an individual decision tree is constructed
for each sample. Each decision tree will generate an output,
as shown in the figure. The final output is considered based
on majority voting. In the below figure, you can see that the
majority decision tree gives output as an apple when
compared to a banana, so the final output is taken as an
apple.
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Random Forest
38
The following steps explain the working Random
Forest Algorithm:
Step 1: Select random samples from a given data or
training set.
Step 2: This algorithm will construct a decision
tree for every training data.
Step 3: Voting will take place by averaging the
decision tree.
Step 4: Finally, select the most voted prediction
result as the final prediction result.
This combination of multiple models is called
Ensemble.
1.Bagging
2. Boosting
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Bagging
 Bagging, also known as Bootstrap Aggregation, is the ensemble technique used by
random forest.
 Bagging chooses a random sample/random subset from the entire data set.
 This random sample is called Bootstrap sample.
 Here the bootstrap sample is taken from actual data (Bootstrap sample 01,
Bootstrap sample 02, and Bootstrap sample 03) with a replacement which means
there is a high possibility that each sample may be a duplicated sample.
 The model (Model 01, Model 02, and Model 03) obtained from this bootstrap
sample is trained independently and Each model generates results.
 The final output is based on majority voting after combining the results of all
models.
 This step which involves combining all the results and generating output based on
majority voting, is known as aggregation.
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Bagging
41
Boosting
 It is an ensembling technique that attempts to build a strong
classifier from the number of weak classifiers.
 It is done by building a model by using weak models in series.
 Firstly, a model is built from the training data.
 Then the second model is built which tries to correct the errors
present in the first model.
 This procedure is continued and models are added until either
the complete training data set is predicted correctly or the
maximum number of models is added.
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Why use Random Forest?
It takes less training time as compared to other algorithms.
•It predicts output with high accuracy, even for the large dataset it runs
efficiently.
•It can also maintain accuracy, when a large proportion of data is missing.
Applications of Random Forest
There are mainly four sectors where Random forest mostly used:
1. Banking: Banking sector mostly uses this algorithm for the identification of
loan risk.
2. Medicine: With the help of this algorithm,
risks of the disease can be
identified.
3.Land Use: We can identify the areas of similar land use by this algorithm.
4.Marketing: Marketing trends can be identified using this algorithm.
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Advantages of Random Forest
•It is capable of performing both Classification and Regression
tasks.
•It is capable of handling large datasets with high dimensionality.
•It enhances the accuracy of the model and prevents the
overfitting issue.
Disadvantages of Random Forest
•Although random forest can be used for both classification and
regression tasks, it is not more suitable for Regression tasks.
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Support Vector Machines
SVM
is
a
model,
which
can
do
linear
classification as well as regression.
Primarily, it is used for
in Machine Learning.
2Dimensional
Feature Space
In SVM, a model is built to discriminate the data
instances belonging to different classes.
Let us assume for the sake of simplicity that the
data instances are linearly separable.
In this case, when mapped in a two- dimensional
space, the data instances belonging to different
classes fall in different sides of a straight line
drawn in the two-dimensional space as depicted in
Figure.
45
If the same concept is extended to a multi
dimensional
feature
space,
the
straight
line
dividing data instances belonging to different
classes transforms to a hyperplane as depicted in
Figure.
46
 The goal of the SVM algorithm is to create the best
line or decision boundary that
into classes so that we can easily
put the new data point in the correct category in
the future. This best decision boundary is called a
hyperplane.
 SVM chooses the extreme points/vectors that help
 These extreme cases are called as support vectors,
and hence algorithm is termed as Support Vector
Machine.
47
Hyperplane: There can be multiple lines/decision
boundaries to segregate the classes in n-dimensional
space, but we need to find out the best decision
boundary that helps to classify the data points. This
best boundary is known as the hyperplane of SVM.
The dimensions of the hyperplane depend on the
features present in the dataset, which means if there
are 2 features (as shown in image), then hyperplane
will be a straight line. And if there are 3 features,
then hyperplane will be a 2-dimension plane.
We always create a hyperplane that has a maximum
margin, which means the maximum distance between
the data points.
Support Vectors: The data points or vectors that are
the closest to the hyperplane and which affect the
position of the hyperplane are termed as Support
Vector. Since these vectors support the hyperplane,
hence called a Support vector.
 Consider the below diagram in which there are two
different categories that are classified using a decision
boundary or hyperplane:
The working of the SVM algorithm can be understood by using
an example. Suppose we have a dataset that has two tags
(green and blue), and the dataset has two features x1 and x2.
We want a classifier that can classify the pair(x1, x2) of
coordinates in either green or blue. Consider the below
image:
So as it is 2-d
space by just
using a straight
line, we can easily
separate
these
two classes. But
there can be
multiple lines that
can
separate
these
classes.
Consider
the
below image:
Hence, the SVM algorithm helps to find the best line or decision boundary; this
best boundary or region is called as a hyperplane. SVM algorithm finds the closest
point of the lines from both the classes. These points are called support vectors.
The distance between the vectors and the hyperplane is called as margin. And the
goal of SVM is to maximize this margin. The hyperplane with maximum margin is
called the optimal hyperplane.