1. MACHINE LEARNING AND DATA SCIENCES
III-B.Tech.-II-Sem
Subject Code: CS-PCC-322
Unit-II: Machine Learning 10 hours
Overview of Machine learning concepts – Over fitting and train/test
splits, Types of Machine learning – Supervised, Unsupervised,
Reinforced learning, Introduction to Bayes Theorem, Linear Regression-
model assumptions, regularization (lasso, ridge, elastic net),
Classification and Regression algorithms- Naïve Bayes, K-Nearest
Neighbors, logistic regression, support vector machines (SVM), decision
trees, and random forest, Classification Errors..
Dr.S.Dhanalakshmi

2. Introduction to Machine Learning (Definition)
Machine learning is a subfield of artificial intelligence (AI). The goal of machine
learning generally is to understand the structure of data and fit that data into models
that can be understood and utilized by people.
•Machine Learning is the most popular technique of predicting the future or
classifying information to help people in making necessary decisions.
•Machine Learning algorithms are trained over instances or examples through which
they learn from past experiences and also analyze the historical data.
•The whole concept of machine learning is figuring out ways in which we can teach a
computer to perform a task without a need to provide explicit instructions.
•The term machine learning was coined in 1959 by Arthur SamuelThe term machine
learning was coined in 1959 by Arthur Samuel, an American IBMerThe term machine
learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the
field of computer gamingThe term machine learning was coined in 1959 by Arthur
Samuel, an American IBMer and pioneer in the field of computer gaming and artificial
intelligence.
•Machine learning, which deals with the information world. Machines use data to
learn, and machine learning aims to derive meaning from that data. Machine learning
uses statistical methods to enable machines to improve with machines. A subset of
machine learning is deep learning, which enables multi-layer neural networks.

3. Contd.,

4. Contd.,

5. Overview of Machine Learning Concepts
AI is the greater pool that contains
an amalgamation of all (AI enables machines
to think without any human intervention)
Machine Learning is a part of Artificial
Intelligence that involves implementing
algorithms that are able to learn from
the data or previous instances and are able
to perform tasks without explicit
instructions. (subset of AI that uses
statistical learning algorithms that learn
pattern in data over time)
Deep learning is a component of a broader
family of machine learning
methods supported artificial neural
networks with representation learning.
(subset of ML that filters the data through
multiple layers)

6. Essential Components of Machine Learning
• Representation (what the model looks like)
• Evaluation (how do we differentiate good models from bad ones)
• Optimization ( what is our process for finding the good models among all the
possible models)

7. Contd.,

8. Contd.,

9. TRADITIONAL PROGRAMMING VS MACHINE LEARNING
Traditional Programming
Traditional programming is a manual process — meaning a person
(programmer) creates the program. But without anyone programming the
logic, one has to manually formulate or code rules. We have the input data,
and someone (programmer) coded a program that uses that data and runs
on a computer to produce the desired output.
Machine Learning
Machine Learning, on the other hand, the input data and output are fed to an algorithm
to create a program.
In Traditional programming one has to manually formulate/code rules while in Machine
Learning the algorithms automatically formulate the rules from the data, which is very
powerful. .

10. Contd.,

11. Terminology in Machine Learning
Model: Also known as “hypothesis”, a machine learning model is the mathematical
representation of a real-world process. A machine learning algorithm along with the training
data builds a machine learning model.
Feature: A feature is a measurable property or parameter of the data-set.
Feature Vector: It is a set of multiple numeric features. We use it as an input to the machine
learning model for training and prediction purposes.
Training: An algorithm takes a set of data known as “training data” as input. The learning
algorithm finds patterns in the input data and trains the model for expected results (target).
The output of the training process is the machine learning model.
Prediction: Once the machine learning model is ready, it can be fed with input data to
provide a predicted output.
Target (Label): The value that the machine learning model has to predict is called the target
or label.
Overfitting: When a massive amount of data trains a machine learning model, it tends to
learn from the noise and inaccurate data entries. Here the model fails to characterize the
data correctly.
Underfitting: It is the scenario when the model fails to decipher the underlying trend in the
input data. It destroys the accuracy of the machine learning model. In simple terms, the
model or the algorithm does not fit the data well enough

12. How Does Machine Learning Works

13. Steps to Build for ML Model

14. Contd.,
1. Data collection
Machine learning requires training data, a lot of it (either labelled, meaning
supervised learning or not labelled, meaning unsupervised learning).
2. Data preparation
Raw data alone is not very useful. The data needs to be prepared, normalized,
de-duplicated and errors and bias need to be removed. Visualisation of the data can be
used to look for patterns and outliers to see if the right data has been collected or if data is
missing.
Cleaning and Visualizing Data

15. Contd.,
3. Choosing a model
Based on the collected data with relevant to the task choose a model. Its mainly
used for various models, linear regression, logistic regression, decision trees,
K-means, principal component analysis (PCA), Support Vector Machines (SVM),
Naïve Bayes, Random Forest and Neural Networks. If your model is suited for
numerical or categorical data and choose accordingly.
Model Applications
Logistic Regression Price prediction
Fully connected networks Classification
Convolutional Neural Networks Image processing
Recurrent Neural Networks Voice recognition
Random Forest Fraud Detection
Reinforcement Learning Learning by trial and error
Generative Models Image creation
K-means Segmentation
k-Nearest Neighbors Recommendation systems
Bayesian Classifiers Spam and noise filtering

16. Contd.,
4. Training
Training is the most important step in machine learning. In training, you pass the
prepared data to your machine learning model to find patterns and make
predictions. It results in the model learning from the data so that it can accomplish
the task set. Over time, with training, the model gets better at predicting.
5. Evaluation Evaluating a model
After training the model comes evaluating the model. This entails testing the
machine learning against an unused control dataset to see how it performs. This
might be representative of how the model works in the real world, but this does
not have to be the case. The larger the number of variables in the real world, the
bigger to training and test data should be.

17. Contd.,
6. Parameter tuning
After evaluating your model, you should test the originally set parameters to
improve the AI. Increasing the number of training cycles can lead to more accurate
results. However, you should define when a model is good enough as otherwise,
you will continue to tweak the model. This is an experimental process.
7. Prediction
Once you have gone through the process of collecting data, preparing the data,
selecting the model, training and evaluating the model and tuning the parameters,
it is time to answer questions using predictions. These can be all kinds of
predictions, ranging from image recognition to semantics to predictive analytics.

18. Machine Learning Examples

19. Applications of Machine Learning

20. Typical Machine Learning Process
• Training data. This type of data builds up the machine learning algorithm. The
data scientist feeds the algorithm input data, which corresponds to an expected
output. The model evaluates the data repeatedly to learn more about the
data’s behavior and then adjusts itself to serve its intended purpose.
• Validation data. During training, validation data infuses new data into the
model that it hasn’t evaluated before. Validation data provides the first test
against unseen data, allowing data scientists to evaluate how well the model
makes predictions based on the new data. Not all data scientists use validation
data, but it can provide some helpful information to optimize hyper
parameters, which influence how the model assesses data.
• Test data. After the model is built, testing data once again validates that it can
make accurate predictions. If training and validation data include labels to
monitor performance metrics of the model, the testing data should be
unlabeled. Test data provides a final, real-world check of an unseen dataset to
confirm that the ML algorithm was trained effectively.

21. Typical Machine Learning Process
overfitting
Creating a model that matches the training data so closely that the model fails to make
correct predictions on new data.

22. Over fitting and train/test splits

23. Over fitting and train/test splits
Train/Test is a method to measure the accuracy of your model.
• It is called Train/Test because you split the the data set into two sets: a
training set and a testing set. 80% for training, and 20% for testing.
• You train the model using the training set, You test the model using the testing
set.
• Train the model means create the model, Test the model means test the
accuracy of the model.
• Nevertheless, common split percentages include:
Train: 80%, Test: 20%
Train: 67%, Test: 33%
Train: 50%, Test: 50%

24. Splitting
DataSets
• To use dataset in machine learning the dataset is first split into a training
and test set.
• The training set is used to train the model
• The test set is used to test the accuracy of the model

25. Splitting of Dataset
•Method 1

26. •Second Method

27. Data Imbalance _Overfitting
• If the training data is overly unbalanced, then the model will predict a non
meaningful result
• For example, if a model is a binary classifier (eg. Cat vs Dog) and nearly all
the samples are of the same label (cat) then the model will simply learn
that everything is a that label (Cat).
• This is called Overfitting. To prevent overfitting, there needs to be fairly
equal distribution of training samples for each classification, or range if
label is a real value

29. Machine Learning Techniques

30. Machine Learning Techniques

31. Types of Machine Learning
Machine Learning Techniques
• Supervised Machine Learning
• Semi-supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Machine Learning

32. Machine Learning Techniques
Machine learning, tasks are generally classified into broad categories. These
categories are based on how learning is received or how feedback on the learning is
given to the system developed.
•Two of the most widely adopted machine learning methods are supervised learning
which trains algorithms based on example input and output data that is labeled by
humans, and unsupervised learning which provides the algorithm with no labeled
data in order to allow it to find structure within its input data. The semi-supervised
models use both labeled and unlabeled data for training. reinforcement learning has
a feedback type of algorithm (the machine learn on its own)

33. Contd.,

34. Contd.,

35. Supervised Machine Learning
1. It is a type of learning in which both input and desired output data are provided.
2. Input and output data are labeled for classification to provide a learning basis for
future data processing. (A model based on supervised learning would require both
previous data and the previous results as input. By training with this data, the model
helps in predicting results that are more accurate.
3. This algorithm consist of a target / outcome variable (or dependent variable) which
is to be predicted from a given set of predictors (independent variables).
4. Using these set of variables, we generate a function that map inputs to desired
outputs. The training process continues until the model achieves a desired level of
accuracy on the training data
5. Supervised learning has methods like classification, regression, naïve bayes theorem,
SVM, KNN, decision tree, etc.
.

36. Contd.,

37. Contd.,

38. Contd.,
Supervised learning is classified into two categories of algorithms:
Classification: A classification problem is when the output variable is a category,
such as “Red” or “blue” or “disease” and “no disease”.
Regression: A regression problem is when the output variable is a real value,
such as “dollars” or “weight”.
Types:-
Regression
Logistic Regression
Classification
Naive Bayes Classifiers
K-NN (k nearest neighbors)
Decision Trees
Support Vector Machine
Advantages:-
•Supervised learning allows collecting data and produces data output from
previous experiences.
•Helps to optimize performance criteria with the help of experience.
•Supervised machine learning helps to solve various types of real-world
computation problems.
Disadvantages:-
•Classifying big data can be challenging.
•Training for supervised learning needs a lot of computation time. So, it requires a
lot of time.

39. Contd.,

40. Unsupervised Machine Learning
1. Unsupervised learning needs no previous data as input. It is the method that allows the
model to learn on its own using the data, which you give. Here, the data is not labelled,
but the algorithm helps the model in forming clusters of similar types of data. For
example, if we have the data of dogs and cats, the model will process and train itself
with the data. Since it has no previous experience of the data, it will form clusters based
on similarities of features.
2. it trains the model by making it learn about the data and work on it from the very start.
Also, after the data is clustered and classified, we can easily label the data in separate
categories as the data is already solved now.

41. Unsupervised Machine Learning

42. Unsupervised Machine Learning

43. Contd.,
Unsupervised learning is classified into two categories of algorithms:
1. Clustering: A clustering problem is where you want to discover the inherent
groupings in the data, such as grouping customers by purchasing behavior.
2. Association: An association rule learning problem is where you want to discover
rules that describe large portions of your data, such as people that buy X also
tend to buy Y.
Types of Unsupervised Learning:-
• Clustering
• Exclusive (partitioning)
• Agglomerative
• Overlapping
• Probabilistic
Clustering Types:-
1. Hierarchical clustering
• K-means clustering
• Principal Component Analysis
• Singular Value Decomposition
• Independent Component Analysis

44. Contd.,
Advantages of Unsupervised Learning
1.we sometimes choose unsupervised learning in place of supervised learning. Here
are some of the advantages:
2.Labeling of data demands a lot of manual work and expenses. Unsupervised
learning solves the problem by learning the data and classifying it without any
labels.
3.The labels can be added after the data has been classified which is much easier.
4. It is very helpful in finding patterns in data, which are not possible to find using
normal methods.
5.Dimensionality reduction can be easily accomplished using unsupervised learning.
6.unsupervised learning can help to understand raw data.
Disadvantages of Unsupervised Learning
•The result might be less accurate as we do not have any input data to train from.
1.The model is learning from raw data without any prior knowledge.
2.It is also a time-consuming process. The learning phase of the algorithm might
take a lot of time, as it analyses and calculates all possibilities.

45. Contd.,

46. Contd.,

47. Semi-supervised Machine Learning
This is a combination of supervised and unsupervised learning. This method helps to reduce
the shortcomings of both the above learning methods.
In supervised learning, labelling of data is manual work and is very costly as data is huge. In
unsupervised learning, the areas of application are very limited. To reduce these problems,
semi-supervised learning is used.
the model first trains under unsupervised learning. This ensures that most of the unlabeled
data divide into clusters. For the remaining unlabeled data, the generation of labels takes
place and classification carries with ease. This technique is very useful in areas like speech
recognition and analysis, protein classification, text classification, etc. This is a type of
hybrid learning problem. (its working lies between Supervised and Unsupervised techniques.
We use these techniques when we are dealing with data that is a little bit labeled and the rest
large portion of it is unlabeled. We can use the unsupervised techniques to predict labels and
then feed these labels to supervised techniques. This technique is mostly applicable in the
case of image data sets where usually all images are not labeled.)

48. Reinforcement Machine Learning
1. The model keeps on increasing its performance using Reward Feedback to
learn the behavior or pattern. These algorithms are specific to a particular
problem e.g. Google Self Driving car, AlphaGo where a bot competes with
humans and even itself to getting better and better performers of Go Game.
2. Each time we feed in data, they learn and add the data to its knowledge that is
training data. So, the more it learns the better it gets trained and hence
experienced.

50. Reinforcement Machine Learning
1. Reinforcement Learning is a type of learning methodology in ML along with
supervised and unsupervised learning. But, when we compare these three,
reinforcement learning is a bit different than the other two. Here, we take the
concept of giving rewards for every positive result and make that the base of our
algorithm.
2. We can train our dog to perform certain actions, of course, it won’t be an easy task.
You would order the dog to do certain actions and for every proper execution, you
would give a biscuit as a reward. The dog will remember that if it does a certain
action, it would get biscuits. This way it will follow the instructions properly next
time.

51. Reinfocement Learning

52. Types of Data

53. Learning Appraoch

54. Applications

55. Contd.,

56. What are the most common and popular machine learning algorithms?
1. Naïve Bayes Classifier Algorithm (Supervised Learning - Classification)
2. K Means Clustering Algorithm (Unsupervised Learning - Clustering)
3. Support Vector Machine Algorithm (Supervised Learning - Classification)
4. Linear Regression (Supervised Learning/Regression)
5. Logistic Regression (Supervised learning – Classification)
6. Artificial Neural Networks (Reinforcement Learning)
7. Decision Trees (Supervised Learning – Classification/Regression)
8. Random Forests (Supervised Learning – Classification/Regression)
9. Nearest Neighbours (Supervised Learning)

57. Classfication and Regression Algorithms
• Definition of Classification
• Definition of Regression
• Differentiate between Classification and Regression
• Types of Classification Algorithms
– Naïve Bayes Algorithms
– K-Nearest Neighbors Algorithms
– Logistic Regression
– support vector machines (SVM)
– Decision Trees
– Random Forest
Classification Errors

58. Contd.,
Regression and Classification algorithms are Supervised Learning algorithms. Both
the algorithms are used for prediction in Machine learning and work with the
labeled datasets.
The main difference between Regression and Classification algorithms that
Regression algorithms are used to predict the continuous values such as price,
salary, age, etc. and Classification algorithms are used to predict/Classify the
discrete values such as Male or Female, True or False, Spam or Not Spam, etc.

59. Definition of Classification
Classification:
Classification is a process of finding a function which helps in dividing the dataset into
classes based on different parameters. In Classification, a computer program is
trained on the training dataset and based on that training, it categorizes the data
into different classes. The task of the classification algorithm is to find the mapping
function to map the input(x) to the discrete output(y).
Example: The best example to understand the Classification problem is Email Spam
Detection. The model is trained on the basis of millions of emails on different
parameters, and whenever it receives a new email, it identifies whether the email is
spam or not. If the email is spam, then it is moved to the Spam folder.

60. Contd.,
Types of ML Classification Algorithms:
Classification Algorithms can be further divided into the following types:
– Logistic Regression
– K-Nearest Neighbours
– Support Vector Machines
– Naïve Bayes
– Decision Tree Classification
– Random Forest Classification

61. Definition of Regression
Regression is a process of finding the correlations between
dependent and independent variables. It helps in predicting the
continuous variables such as prediction of Market Trends, prediction
of House prices, etc.
The task of the Regression algorithm is to find the mapping function
to map the input variable(x) to the continuous output variable(y).
Example:
Suppose we want to do weather forecasting, so for this, we will use
the Regression algorithm. In weather prediction, the model is trained
on the past data, and once the training is completed, it can easily
predict the weather for future days.

62. Contd.,
Types of Regression Algorithm
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression

63. Regression Algorithm Classification Algorithm
In Regression, the output variable must
be of continuous nature or real value.
In Classification, the output variable must
be a discrete value.
The task of the regression algorithm is to
map the input value (x) with the
continuous output variable(y).
The task of the classification algorithm is
to map the input value(x) with the
discrete output variable(y).
Regression Algorithms are used with
continuous data.
Classification Algorithms are used with
discrete data.
In Regression, we try to find the best fit
line, which can predict the output more
accurately.
In Classification, we try to find the
decision boundary, which can divide the
dataset into different classes.
Regression algorithms can be used to
solve the regression problems such as
Weather Prediction, House price
prediction, etc.
Classification Algorithms can be used to
solve classification problems such as
Identification of spam emails, Speech
Recognition, Identification of cancer cells,
etc.
The regression Algorithm can be further
divided into Linear and Non-linear
Regression.
The Classification algorithms can be
divided into Binary Classifier and
Multi-class Classifier.

64. 1. Naïve Bayes Algorithm
• The Naive Bayes classifier works on the principle of conditional probability, as
given by the Bayes theorem. While calculating the math on probability, we
usually denote probability as P.
• The Bayes theorem gives us the conditional probability of event A, given that
event B has occurred. In this case, the first coin toss will be B and the second
coin toss A. This could be confusing because we've reversed the order of them
and go from B to A instead of A to B.
• Bayes theorem calculates the conditional probability of the occurrence of an
event based on prior knowledge of conditions that might be related to the
event.

65. Contd.,
• Naïve Bayes Classifier is one among the straightforward and best
Classification algorithms which helps in building the fast machine
learning models which will make quick predictions.
• Naive Bayes is one of the powerful machine learning algorithms that is used
for
classification.
• It is an extension of the Bayes theorem wherein each feature
assumes independence. It is used for a variety of tasks such as spam
filtering and other areas of text classification.
Understanding Naive Bayes and Machine Learning
Machine learning falls into two categories:
• Supervised learning and Unsupervised learning
Supervised learning falls into two categories:
• Classification and Regression
Naive Bayes algorithm falls under classification.

66. Contd.,
Naïve Bayes used for,
• Face Recognition - As a classifier, it is used to identify the faces or its other
features, like nose, mouth, eyes, etc.
• Weather Prediction -It can be used to predict if the weather will be good or
bad.
• Medical Diagnosis - Doctors can diagnose patients by using the information
that the classifier provides. Healthcare professionals can use Naive Bayes to
indicate if a patient is at high risk for certain diseases and conditions, such as
heart disease, cancer, and other ailments.
• News Classification
But it will require less training data

67. Contd.,
Example
Under the day, look for variables, like weekday, weekend, and holiday. For any given
day, check if there are a discount and free delivery. Based on this information, we
can predict if a customer would buy the product or not.
• See a small sample data set of 30 rows, with 15 of them, as shown below:

69. Contd.,
Based on the dataset containing the three input types—day, discount, and free
delivery— the frequency table for each attribute is populated.

70. Contd.,

71. Contd.,

73. Contd.,

74. Contd.,

75. Contd.,

76. Contd.,

77. Contd.,

78. Contd.,

79. 2. K-Nearest Neighbors
The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised
machine learning algorithm that can be used to solve both classification and
regression problems. A supervised machine learning algorithm (as opposed to an
unsupervised machine learning algorithm) is one that relies on labeled input data
to learn a function that produces an appropriate output when given new unlabeled
data.
The KNN algorithm assumes that similar things exist in close proximity. In other
words, similar things are near to each other.
•K-NN algorithm can be used for Regression as well as for Classification but mostly it
is used for the Classification problems.
•K-NN is a non-parametric algorithm, which means it does not make any
assumption on underlying data.
It is also called a lazy learner algorithm because it does not learn from the training
set immediately instead it stores the dataset and at the time of classification, it
performs an action on the dataset.

80. Contd.,
Why do we need a K-NN Algorithm?
Suppose there are two categories, i.e., Category A and Category B, and we have a
new data point x1, so this data point will lie in which of these categories.
To solve this type of problem, we need a K-NN algorithm. With the help of K-NN,
we can easily identify the category or class of a particular dataset.
K-Nearest Neighbor is a classification and prediction algorithm that is used to
divide data into classes based on the distance between the data points. K-Nearest
Neighbor assumes that data points which are close to one another must be similar
and hence, the data point to be classified will be grouped with the closest cluster.

81. Contd.,

82. Contd.,

83. Contd.,
57 KG, 170 CM IS NORMAL

84. 3. Logistic Regression
Logistic Regression is a Machine Learning classification algorithm that is used to
predict the probability of a categorical dependent variable. In logistic regression,
the dependent variable is a binary variable(result in binary format) that contains
data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.).
•It is a technique to analyse a data-set which has a dependent variable and one or
more independent variables to predict the outcome in a binary variable,
meaning it will have only two outcomes.
•The dependent variable is categorical in nature. Dependent variable is also
referred as target variable and the independent variables are called
the predictors
Logistic regression is a supervised learning algorithm used to predict a
dependent categorical target variable but is used to classify samples; Therefore,
it falls under the classification algorithm.
.

85. Contd.,
Linear Regression vs Logistic Regression
• Both are supervised learning models and make use of labeled data for
making predictions.
• Linear regression is used for regression(prediction)problems
whereas Logistic regression can be used in both classification and regression
problems but is widely used as a classification algorithm
• But the main difference between them is how they are being used. The Linear
Regression is used for solving Regression problems whereas Logistic
Regression is used for solving the Classification problems. The description of
both the algorithms is given below along with difference table.

86. Contd.,
Type of Logistic Regression:
On the basis of the Dependent variable, Logistic Regression can be classified into three
types:
• Binomial: There can be only two possible types of the dependent variables, such as 0
or 1, Pass or Fail, Purchased or Not Purchased, Tall or Short, Fat or Slim, Rock or Mine,
etc.
• Multinomial: There can be 3 or more possible unordered types of the dependent
variable, such as apple, banana, orange or cat, dog, goat, sheep or Delhi, Mumbai,
Bangalore, Calcutta.
• Ordinal: In ordinal Logistic regression, there can be 3 or more possible ordered types
of dependent variables, such as High, medium, low, or ratings of a restaurant from 1
to 5 or the intensity of the light, or a 5 points Likert scale, etc

87. Contd.,
Linear Regression Logistic Regression
Linear regression is used to predict the
continuous dependent variable using a given
set of independent variables.
Logistic Regression is used to predict the
categorical dependent variable using a given
set of independent variables.
Linear Regression is used for solving
Regression problem.
Logistic regression is used for solving
Classification problems.
In Linear regression, we predict the value of
continuous variables.
In logistic Regression, we predict the values
of categorical variables.
In linear regression, we find the best fit line,
by which we can easily predict the output.
In Logistic Regression, we find the S-curve by
which we can classify the samples.
Least square estimation method is used for
estimation of accuracy.
Maximum likelihood estimation method is
used for estimation of accuracy.
The output for Linear Regression must be a
continuous value, such as price, age, etc.
The output of Logistic Regression must be a
Categorical value such as 0 or 1, Yes or No,
etc.
In Linear regression, it is required that
relationship between dependent variable and
independent variable must be linear.
In Logistic regression, it is not required to
have the linear relationship between the
dependent and independent variable.
In linear regression, there may be collinearity
between the independent variables.
In logistic regression, there should not be
collinearity between the independent
variable.

88. 4. Support Vector Machines (SVM)
Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms,
which is used for Classification as well as Regression problems. However, primarily, it is used
for Classification problems in Machine Learning.
The goal of the SVM algorithm is to create the best line or decision boundary that can
segregate n-dimensional space 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.
•They build upon basic ML algorithms and add features that make them more efficient at
various tasks. It can be used in a variety of tasks, including anomaly detection, handwriting
recognition, and text classification. Because of their flexibility, high performance, and
compute efficiency,

89. Contd.,

90. Contd.,

91. Contd.,
Example: SVM can be understood with the example that we have used in the KNN
classifier. Suppose we see a strange cat that also has some features of dogs, so if
we want a model that can accurately identify whether it is a cat or dog, so such a
model can be created by using the SVM algorithm. We will first train our model
with lots of images of cats and dogs so that it can learn about different features of
cats and dogs, and then we test it with this strange creature. So as support vector
creates a decision boundary between these two data (cat and dog) and choose
extreme cases (support vectors), it will see the extreme case of cat and dog. On the
basis of the support vectors, it will classify it as a cat.

92. Contd.,
SVM can be of two types:
• Linear SVM: Linear SVM is used for linearly separable data, which means if a
dataset can be classified into two classes by using a single straight line, then such
data is termed as linearly separable data, and classifier is used called as Linear
SVM classifier.
• Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which
means if a dataset cannot be classified by using a straight line, then such data is
termed as non-linear data and classifier used is called as Non-linear SVM
classifier.
Linear Separable Data Non-Linear Separable Data

93. Contd.,
• When we can easily separate data with hyperplane by drawing a straight line is
Linear SVM. When we cannot separate data with a straight line we use Non –
Linear SVM. In this, we have Kernel functions. They transform non-linear
spaces into linear spaces. It transforms data into another dimension so that the
data can be classified.
• It transforms two variables x and y into three variables along with z. Therefore,
the data have plotted from 2-D space to 3-D space. Now we can easily classify
the data by drawing the best hyperplane between them.
Linear SVM Non-Linear SVM
It can be easily separated with a linear
line.
It cannot be easily separated with a
linear line.
Data is classified with the help of
hyperplane.
We use Kernels to make
non-separable data into separable
data.
Data can be easily classified by
drawing a straight line.
We map data into high dimensional
space to classify.
Linear SVM vs Non-Linear SVM

94. Contd.,

95. Contd.,
The working of the SVM algorithm (LINEAR SVM)
1.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
2.So as it is 2-d space so by just using a straight line, we can easily separate these two classes.
But there can be multiple lines that can separate these classes.
3.Hence, the SVM algorithm helps to find the best line or decision boundary; this best
boundary or region is called as a hyperplane.
4.SVM algorithm finds the closest point of the lines from both the classes. These points are
called support vectors.
5.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.

96. Contd.,
Non-Linear SVM:
1.If data is linearly arranged, then we can separate it by using a straight line, but for non-linear
data, we cannot draw a single straight line.
2. So to separate these data points, we need to add one more dimension. For linear data,
we have used two dimensions x and y, so for non-linear data, we will add a third dimension
z. It can be calculated as: z=x2
+y2,
By adding the third dimension, the sample space will
become as

97. Contd.,
3. So now, SVM will divide the datasets into classes in the following way.
4. Since we are in 3-d Space, hence it is looking like a plane parallel to the x-axis. If we
convert it in 2d space with z=1, then it will become as: we get a circumference of radius
1 in case of non-linear data.

98. Contd.,
Advantages of SVM
• Good for smaller cleaner datasets.
• Accurate results.
• Useful for both linearly separable data and non – linearly separable data.
• Effective in high dimensional spaces.
Disadvantages of SVM
• Not suitable for large datasets, as the training time can be too much.
• Not so effective on a dataset with overlapping classes.
• Picking the right kernel can be computationally intensive.
Applications of SVM
• Sentiment analysis.
• Spam Detection.
• Handwritten digit recognition.
• Image recognition challenges

99. 5. Decision Trees
• Decision Tree is a Supervised learning technique that can be used for both
classification and Regression problems, but mostly it is preferred for solving
Classification problems. It is a tree-structured classifier, where internal nodes
represent the features of a dataset, branches represent the decision
rules and each leaf node represents the outcome.
• In a Decision tree, there are two nodes, which are the Decision Node and Leaf
Node. Decision nodes are used to make any decision and have multiple branches,
whereas Leaf nodes are the output of those decisions and do not contain any
further branches.
• The decisions or the test are performed on the basis of features of the given
dataset.
• It is a graphical representation for getting all the possible solutions to a
problem/decision based on given conditions.
• It is called a decision tree because, similar to a tree, it starts with the root node,
which expands on further branches and constructs a tree-like structure.
• In order to build a tree, we use the CART algorithm, which stands
for Classification and Regression Tree algorithm.
• A decision tree simply asks a question, and based on the answer (Yes/No), it
further split the tree into subtrees.

100. Contd.,
A decision tree can contain categorical data (YES/NO) as well as numeric data.
Decision Tree Terminologies
• Root Node: Root node is from where the decision tree starts. It represents the entire
dataset, which further gets divided into two or more homogeneous sets.
• Leaf Node: Leaf nodes are the final output node, and the tree cannot be segregated
further after getting a leaf node.
• Splitting: Splitting is the process of dividing the decision node/root node into sub-nodes
according to the given conditions.
• Branch/Sub Tree: A tree formed by splitting the tree.
• Pruning: Pruning is the process of removing the unwanted branches from the tree.
• Parent/Child node: The root node of the tree is called the parent node, and other nodes
are called the child nodes.

101. Contd.,
Attribute Selection Measures
While implementing a Decision tree, the main issue arises that how to select the
best attribute for the root node and for sub-nodes. So, to solve such problems
there is a technique which is called as Attribute selection measure or ASM. By
this measurement, we can easily select the best attribute for the nodes of the
tree. There are two popular techniques for ASM,
• Information Gain
• Gini Index
1. Information Gain:
• Information gain is the measurement of changes in entropy after the
segmentation of a dataset based on an attribute.
• It calculates how much information a feature provides us about a class.
• According to the value of information gain, we split the node and build the
decision tree.
• A decision tree algorithm always tries to maximize the value of information
gain, and a node/attribute having the highest information gain is split first. It
can be calculated using the below formula:
Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature)

102. Contd.,
Entropy: Entropy is a metric to measure the impurity in a given attribute. It
specifies randomness in data. Entropy can be calculated as:
Entropy(s)= -P(yes)log2 P(yes)- P(no) log2 P(no)
– S= Total number of samples
– P(yes)= probability of yes
– P(no)= probability of no
2. Gini Index:
•Gini index is a measure of impurity or purity used while creating a decision tree in
the CART(Classification and Regression Tree) algorithm.
•An attribute with the low Gini index should be preferred as compared to the high
Gini index.
•It only creates binary splits, and the CART algorithm uses the Gini index to create
binary splits.
•Gini index can be calculated using the below formula:
Gini Index= 1- ∑j
Pj
2

103. DataSet

123. Contd.,
Advantages of Decision Tree
1. Clear Visualization
2. Simple and easy to understand
3. Decision Tree can be used for both classification and regression problems.
4. Decision Tree can handle both continuous and categorical variables.
5. No feature scaling required
6. Handles non-linear parameters efficiently
7. Decision Tree can automatically handle missing values.
8. Decision Tree is usually robust to outliers and can handle them automatically.
9. Less Training Period
Disadvantages of Decision Tree
1. Overfitting
2. High variance
3. Unstable
4. Affected by noise
5. Not suitable for large datasets

124. 6. Random Forest
• Random Forest 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.
• "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.
Random forest is a commonly-used machine learning algorithm trademarked by
Leo Breiman and Adele Cutler, which combines the output of multiple decision
trees to reach a single result. Its ease of use and flexibility have fueled its
adoption, as it handles both classification and regression problems.

125. Contd.,

126. Contd.,
• Random forest algorithms have three main hyperparameters, which need to be
set before training.
• These include node size, the number of trees, and the number of features
sampled. From there, the random forest classifier can be used to solve for
regression or classification problems.
• The random forest algorithm is made up of a collection of decision trees, and
each tree in the ensemble is comprised of a data sample drawn from a training
set with replacement, called the bootstrap sample. Of that training sample,
one-third of it is set aside as test data, known as the out-of-bag (oob) sample,
which we’ll come back to later. Another instance of randomness is then injected
through feature bagging, adding more diversity to the dataset and reducing the
correlation among decision trees. Depending on the type of problem, the
determination of the prediction will vary. For a regression task, the individual
decision trees will be averaged, and for a classification task, a majority vote—i.e.
the most frequent categorical variable—will yield the predicted class. Finally, the
oob sample is then used for cross-validation, finalizing that prediction.

127. Contd.,
a) Working of Random Forest Algorithm
Before understanding the working of the random forest we must look into the
ensemble technique.
Ensemble Learning
1.means Groups
2.In Ensemble learning individual models come together and bring forth a model that
is more accurate )
simply means combining multiple models. Thus a collection of models is used to
make predictions rather than an individual model.
Why use Ensemble Models:
1.Better accuracy (low error)
2.Higher consistency (avoid overfitting)
•Reduce bias and variance errors

128. Contd.,
Ensemble uses two types of methods:
1. Bagging– It creates a different training subset from sample training data with
replacement & the final output is based on majority voting. For example, Random
Forest.
2. Boosting– It combines weak learners into strong learners by creating sequential
models such that the final model has the highest accuracy. For example, ADA
BOOST, XG BOOST
Random Forest Algorithm working on Bagging principle

129. Contd.,
1. Bagging
Bagging, also known as Bootstrap Aggregation is the ensemble technique used by
random forest. Bagging chooses a random sample from the data set. Hence each
model is generated from the samples (Bootstrap Samples) provided by the Original
Data with replacement known as row sampling. This step of row sampling with
replacement is called bootstrap. Now each model is trained independently which
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.

130. Contd.,
Bagging – various models are built in parallel on various samples and then the Various
models vote to give the final model and hence prediction

131. Contd.,
2. Boosting – (it is a process that uses a set of machine learning algorithms to combine
weak learner to form strong learners in order to increase the accuracy of the model)
Boosting is an ensemble modeling technique that attempts to build a
1. 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.
2. 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 are added.
1. Little variation on bagging
2. Selecting points which give wrong predictions

132. Contd.,
How Does Boosting Algorithm Work
The basic principle behind the working of the boosting algorithm is to generate
multiple weak learners and combine their predictions to form one strong rule
Decision stumps – is nothing but , single level decision tree, that tries to
classify the data points, and then equal weightage given to all data points

133. Contd.,
Key Benefits
•Reduced risk of overfitting
• Provides flexibility
•Easy to determine feature importance
Key Challenges
•Time-consuming process
•Requires more resources
•More complex
Advantages and Disadvantages of Random Forest
•It reduces overfitting in decision trees and helps to improve the accuracy
•It is flexible to both classification and regression problems
•It works well with both categorical and continuous values
•It automates missing values present in the data
•Normalising of data is not required as it uses a rule-based approach.
However, despite these advantages, a random forest algorithm also has some drawbacks.
•It requires much computational power as well as resources as it builds numerous trees to
combine their outputs.
•It also requires much time for training as it combines a lot of decision trees to determine the
class.
•Due to the ensemble of decision trees, it also suffers interpretability and fails to determine
the significance of each variable.

134. Contd.,
S.N
O
Bagging Boosting
1.
The simplest way of combining
predictions that belong to the same
type.
A way of combining predictions that
belong to the different types.
2. Aim to decrease variance, not bias. Aim to decrease bias, not variance.
3. Each model receives equal weight.
Models are weighted according to their
performance.
4. Each model is built independently.
New models are influenced by the
performance of previously built models.
5.
Different training data subsets are
randomly drawn with replacement from
the entire training dataset.
Every new subset contains the elements
that were misclassified by previous
models.
6.
Bagging tries to solve the over-fitting
problem.
Boosting tries to reduce bias.
7.
If the classifier is unstable (high
variance), then apply bagging.
If the classifier is stable and simple (high
bias) the apply boosting.
8.
Example: The Random forest model uses
Bagging.
Example: The Ada Boost uses Boosting
techniques

135. Contd.,
Decision trees Random Forest
1. Decision trees normally suffer from the
problem of overfitting if it’s allowed to
grow without any control.
1. Random forests are created from
subsets of data and the final output is
based on average or majority ranking and
hence the problem of overfitting is taken
care of.
2. A single decision tree is faster in
computation.
2. It is comparatively slower.
3. When a data set with features is taken
as input by a decision tree it will formulate
some set of rules to do prediction.
3. Random forest randomly selects
observations, builds a decision tree and
the average result is taken. It doesn’t use
any set of formulas.