AI & ML INTRODUCTION OF AI AND ML FOR LEARING BASICS

BCA-V
Artificial Intelligence and Machine Learning
BCA504A
Mrs. Ashu Nayak
Assistant Professor
Department of CS & IT
Kalinga University
Naya Raipur (C.G.), India
BCA504A Artificial Intelligence & Machine Learning 1

CONTENTS
• Kernel Method
• Support Vector Machine Algorithm
• Hyperplane and Support Vectors in the SVM algorithm
• Ensemble learning
• Architecture of Stacking
• Ada Boost algorithm
• Cross-Validation in Machine Learning
• Dimensionality Reduction
• Feature Selection
• Principal Component Analysis
• Multidimensional Scaling
• Linear Discriminant Analysis
2
BCA504A Artificial Intelligence & Machine Learning

LECTURE PLAN
Lecture No. Topics to be covered Slide No.
L-1 Kernel Method 8-14
L-2 Support Vector Machine Algorithm 15-18
L-3 Hyperplane and Support Vectors in the SVM algorithm 19-20
L-4 Ensemble learning 21-25
L-5 Architecture of Stacking 26-28
L-6 Ada Boost algorithm 29-30
L-7 Cross-Validation in Machine Learning 31-32
L-8 Dimensionality Reduction 33-34
L-9 Feature Selection 35-37
L-10 Principal Component Analysis 38
L-11 Multidimensional Scaling 39-40
L-12 Linear Discriminant Analysis 41-42
Quiz 43-53
3

Unit- 5
4

What is Kernel Method?
A set of techniques known as kernel methods are used in machine
learning to address classification, regression, and other prediction
issues. They are built around the idea of kernels, which are functions
that gauge how similar two data points are to one another in a high-
dimensional feature space.

Characteristics of Kernel Function
• Mercer's condition: A kernel function must satisfy Mercer's
condition to be valid. This condition ensures that the kernel function is
positive semi definite, which means that it is always greater than or
equal to zero.
• Positive definiteness: A kernel function is positive definite if it is
always greater than zero except for when the inputs are equal to each
other.
• Non-negativity: A kernel function is non-negative, meaning that it
produces non-negative values for all inputs.

• Symmetry: A kernel function is symmetric, meaning that it produces
the same value regardless of the order in which the inputs are given.
• Reproducing property: A kernel function satisfies the reproducing
property if it can be used to reconstruct the input data in the feature
space.
• Smoothness: A kernel function is said to be smooth if it produces a
smooth transformation of the input data into the feature space.
• Complexity: The complexity of a kernel function is an important
consideration, as more complex kernel functions may lead to over
fitting and reduced generalization performance.

Linear Kernel
• A linear kernel is a type of kernel function used in machine learning,
including in SVMs (Support Vector Machines). It is the simplest and
most commonly used kernel function, and it defines the dot product
between the input vectors in the original feature space.
• The linear kernel can be defined as:
• K(x, y) = x .y
• Where x and y are the input feature vectors. The dot product of the
input vectors is a measure of their similarity or distance in the original
feature space.

Polynomial Kernel
• A particular kind of kernel function utilised in machine learning, such
as in SVMs, is a polynomial kernel (Support Vector Machines). It is a
nonlinear kernel function that employs polynomial functions to
transfer the input data into a higher-dimensional feature space.
• One definition of the polynomial kernel is:
• Where x and y are the input feature vectors, c is a constant term, and d is the
degree of the polynomial, K(x, y) = (x. y + c)d. The constant term is added to,
and the dot product of the input vectors elevated to the degree of the
polynomial.

Gaussian (RBF) Kernel
• The Gaussian kernel, also known as the radial basis function (RBF)
kernel, is a popular kernel function used in machine learning,
particularly in SVMs (Support Vector Machines). It is a nonlinear
kernel function that maps the input data into a higher-dimensional
feature space using a Gaussian function.
• The Gaussian kernel can be defined as:
• K(x, y) = exp(-gamma * ||x - y||^2)
• Where x and y are the input feature vectors, gamma is a parameter that
controls the width of the Gaussian function, and ||x - y||^2 is the squared
Euclidean distance between the input vectors.

Laplace Kernel
• The Laplacian kernel, also known as the Laplace kernel or the
exponential kernel, is a type of kernel function used in machine
learning, including in SVMs (Support Vector Machines). It is a non-
parametric kernel that can be used to measure the similarity or
distance between two input feature vectors.
• The Laplacian kernel can be defined as:
• K(x, y) = exp(-gamma * ||x - y||)
• Where x and y are the input feature vectors, gamma is a parameter that
controls the width of the Laplacian function, and ||x - y|| is the L1 norm or
Manhattan distance between the input vectors.

Support Vector Machine Algorithm
• 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.

On the basis of the support vectors, it will classify it as
a cat. Consider the below diagram:
SVM algorithm can be used for Face detection, image classification, text categorization, etc.

Types of SVM
• 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.

Hyperplane and Support Vectors in
the SVM algorithm
• 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.

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.

What is Ensemble learning in Machine
Learning?
• Ensemble learning is one of the most powerful machine learning
techniques that use the combined output of two or more models/weak
learners and solve a particular computational intelligence problem.
E.g., a Random Forest algorithm is an ensemble of various decision
trees combined.
• "An ensembled model is a machine learning model that combines
the predictions from two or more models.”

There are 3 most common ensemble learning
methods in machine learning. These are as follows:
•Bagging
•Boosting
•Stacking

1. Bagging
• Bagging is a method of ensemble modeling, which is primarily used to
solve supervised machine learning problems. It is generally completed
in two steps as follows:
• Bootstrapping: It is a random sampling method that is used to derive samples
from the data using the replacement procedure. In this method, first, random
data samples are fed to the primary model, and then a base learning algorithm
is run on the samples to complete the learning process.
• Aggregation: This is a step that involves the process of combining the output
of all base models and, based on their output, predicting an aggregate result
with greater accuracy and reduced variance.

2. Boosting
• Boosting is an ensemble method that enables each member to learn
from the preceding member's mistakes and make better predictions for
the future. Unlike the bagging method, in boosting, all base learners
(weak) are arranged in a sequential format so that they can learn from
the mistakes of their preceding learner. Hence, in this way, all weak
learners get turned into strong learners and make a better predictive
model with significantly improved performance.

3. Stacking
• Stacking is one of the popular ensemble modeling techniques in
machine learning. Various weak learners are ensembled in a
parallel manner in such a way that by combining them with Meta
learners, we can predict better predictions for the future.

Architecture of Stacking

• The architecture of the stacking model is designed in such as way that
it consists of two or more base/learner's models and a meta-model that
combines the predictions of the base models. These base models are
called level 0 models, and the meta-model is known as the level 1
model. So, the Stacking ensemble method includes original (training)
data, primary level models, primary level prediction, secondary
level model, and final prediction.

Ada Boost algorithm in Machine
Learning:
• AdaBoost is a boosting set of rules that was added with the aid of
Yoav Freund and Robert Schapire in 1996. It is part of a class of
ensemble getting-to-know strategies that aim to improve the overall
performance of gadget getting-to-know fashions by combining the
outputs of a couple of weaker fashions, known as vulnerable,
inexperienced persons or base novices. The fundamental idea at the
back of AdaBoost is to offer greater weight to the schooling instances
that are misclassified through the modern-day model, thereby focusing
on the samples that are tough to classify.

Advantages of AdaBoost
1. Improved Accuracy
AdaBoost can notably improve the accuracy of susceptible, inexperienced persons, even when the usage of
easy fashions. By specializing in misclassified instances, it adapts to the tough areas of the records
distribution.
2. Versatility
AdaBoost can be used with a number of base newbies, making it a flexible set of rules that
may be carried out for unique forms of problems.
3. Feature Selection
It routinely selects the most informative features, lowering the need for giant function
engineering.
4. Resistance to Overfitting
AdaBoost tends to be much less at risk of overfitting compared to a few different
ensemble methods, thanks to its recognition of misclassified instances.

Cross-Validation in Machine Learning
Cross-validation is a technique for validating the model efficiency by training
it on the subset of input data and testing on previously unseen subset of the
input data. We can also say that it is a technique to check how a statistical
model generalizes to an independent dataset.
•Hence the basic steps of cross-validations are: Reserve a subset of the dataset
as a validation set.
•Provide the training to the model using the training dataset.
•Now, evaluate model performance using the validation set. If the model
performs well with the validation set, perform the further step, else check for
the issues.

Methods used for Cross-Validation
• Validation Set Approach
• Leave-P-out cross-validation
• Leave one out cross-validation
• K-fold cross-validation
• Stratified k-fold cross-validation

What is Dimensionality Reduction?
• The number of input features, variables, or columns present in a given
dataset is known as dimensionality, and the process to reduce these
features is called dimensionality reduction.
• Dimensionality reduction technique can be defined as, "It is a way of
converting the higher dimensions dataset into lesser dimensions
dataset ensuring that it provides similar information." These
techniques are widely used in machine learning for obtaining a better
fit predictive model while solving the classification and regression
problems.

Feature Selection Techniques in
Machine Learning:

Need for Feature Selection
• It helps in avoiding the curse of dimensionality.
• It helps in the simplification of the model so that it can be easily
interpreted by the researchers.
• It reduces the training time.
• It reduces overfitting hence enhance the generalization.

Principal Component Analysis(PCA)
• Principal Component Analysis is an unsupervised learning algorithm
that is used for the dimensionality reduction in machine learning. It is
a statistical process that converts the observations of correlated
features into a set of linearly uncorrelated features with the help of
orthogonal transformation. These new transformed features are called
the Principal Components.

Multidimensional Scaling (MDS)
• Multidimensional scaling (MDS) is a dimensionality reduction
technique that is used to project high-dimensional data onto a lower-
dimensional space while preserving the pairwise distances between the
data points as much as possible. MDS is based on the concept of
distance and aims to find a projection of the data that minimizes the
differences between the distances in the original space and the
distances in the lower-dimensional space.

Features of the Multidimensional
Scaling (MDS)
• MDS is based on the concept of distance and aims to find a projection
of the data that minimizes the differences between the distances in the
original space and the distances in the lower-dimensional space.
• MDS can be applied to a wide range of data types, including
numerical, categorical, and mixed data.
• MDS is implemented using numerical optimization algorithms, such
as gradient descent or simulated annealing, to minimize the difference
between the distances in the original and lower-dimensional spaces.

What is Linear Discriminant Analysis
(LDA)?
• Linear Discriminant analysis is one of the most popular
dimensionality reduction techniques used for supervised
classification problems in machine learning. It is also considered a
pre-processing step for modeling differences in ML and applications
of pattern classification.

Extension to Linear Discriminant
Analysis (LDA)
Some of the common real-world applications of Linear discriminant
Analysis are given below:
•Face Recognition
•Medical
•Customer Identification
•For Predictions
•In Learning

Unit-5 (MCQs)
39
Question 1
What is the primary objective of Support Vector Machines (SVM)?
A) Dimensionality reduction
B) Model complexity minimization
C) Maximize margin between classes
D) Regression accuracy improvement
Correct answer: C
Question 2
Which ensemble method emphasizes reducing variance by training models on different subsets of data?
A) Stacking
B) Boosting
C) Bagging
D) Mixture Models
Correct answer: C

Unit-5 (MCQs)
Question 3
In AdaBoost, what happens to the weights of incorrectly classified data points in subsequent iterations?
A) They are increased
B) They are decreased
C) They remain unchanged
D) They are set to zero
Correct answer: A
Question 4
What does the kernel trick in SVM allow?
A) Mapping data to a higher-dimensional space
B) Reducing computational complexity
C) Enhancing visualization
D) Improving model accuracy
Correct answer: A

Unit-5 (MCQs)
Question 5
What is the primary goal of Dimensionality Reduction techniques?
A) Increase model complexity
B) Improve model interpretability
C) Reduce number of features
D) Enhance model training time
Correct answer: C
Question 6
Which technique in Dimensionality Reduction creates a linear combination of features to maximize
class separability?
A) Principal Components Analysis (PCA)
B) Multidimensional Scaling (MDS)
C) Linear Discriminant Analysis (LDA)
D) Subset Selection
Correct answer: C

Unit-5 (MCQs)
Question 7
How does Bagging differ from Boosting?
A) Bagging uses weighted data points
B) Boosting trains models sequentially
C) Bagging focuses on reducing model complexity
D) Boosting combines multiple models
Correct answer: B
Question 8
What is the main advantage of using Ensemble Methods?
A) Lower computational complexity
B) Higher model bias
C) Improved model generalization
D) Reduced model variance
Correct answer: C

Unit-5 (MCQs)
Question 9
Which Ensemble Method uses a weighted average of predictions from multiple models?
A) Stacking
B) Boosting
C) Bagging
D) Mixture Models
Correct answer: B
Question 10
What does AdaBoost aim to improve in each iteration?
A) Model bias
B) Model complexity
C) Model performance on misclassified data
D) Model interpretability
Correct answer: C

Unit-5 (MCQs)
Question 11
How does the Soft Margin Hyperplane handle non-separable data in SVM?
A) By ignoring misclassified points
B) By penalizing misclassified points
C) By reducing the number of support vectors
D) By increasing the margin width
Correct answer: B
Question 12
What is a potential drawback of using AdaBoost in practice?
A) Sensitivity to noisy data
B) Difficulty in training large datasets
C) Overfitting
D) Slow convergence
Correct answer: A

Unit-5 (MCQs)
Question 13
Which technique in Dimensionality Reduction aims to find the best linear combination of features?
A) PCA
B) MDS
C) LDA
D) Subset Selection
Correct answer: C
Question 14
How does Multidimensional Scaling (MDS) differ from PCA?
A) It focuses on variance maximization
B) It deals with categorical data
C) It visualizes data relationships
D) It doesn't involve feature reduction
Correct answer: C

Unit-5 (MCQs)
Question 15
What is the primary focus of Bagging in ensemble methods?
A) Reduce computational cost
B) Increase model diversity
C) Improve model accuracy
D) Handle imbalanced datasets
Correct answer: B
Question 16
How does Stacking differ from other ensemble methods?
A) It uses only one type of base learner
B) It combines predictions using a meta-learner
C) It doesn't utilize multiple models
D) It focuses on model diversity
Correct answer: B

Unit-5 (MCQs)
Question 17
Which ensemble method is prone to overfitting if not carefully tuned?
A) Stacking
B) Bagging
C) Boosting
D) Mixture Models
Correct answer: C
Question 18
What does PCA prioritize when selecting principal components?
A) Features with high variance
B) Features with low variance
C) All features equally
D) Features with high correlation
Correct answer: A

Unit-5 (MCQs)
Question 19
When is Linear Discriminant Analysis (LDA) preferred over PCA?
A) When class separation is important
B) When feature reduction is the goal
C) When dimensionality is high
D) When data is noisy
Correct answer: A
Question 20
How does boosting handle misclassified data points in each subsequent model iteration?
A) Increases their weights
B) Decreases their weights
C) Ignores them
D) Randomizes their weights
Correct answer: A

49

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