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Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Predicting rainfall using ensemble of ensemblesVarad Meru
The Paper was done in a group of three for the class project of CS 273: Introduction to Machine Learning at UC Irvine. The group members were Prolok Sundaresan, Varad Meru, and Prateek Jain.
Regression is an approach for modeling the relationship between data X and the dependent variable y. In this report, we present our experiments with multiple approaches, ranging from Ensemble of Learning to Deep Learning Networks on the weather modeling data to predict the rainfall. The competition was held on the online data science competition portal ‘Kaggle’. The results for weighted ensemble of learners gave us a top-10 ranking, with the testing root-mean-squared error being 0.5878.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Predicting rainfall using ensemble of ensemblesVarad Meru
The Paper was done in a group of three for the class project of CS 273: Introduction to Machine Learning at UC Irvine. The group members were Prolok Sundaresan, Varad Meru, and Prateek Jain.
Regression is an approach for modeling the relationship between data X and the dependent variable y. In this report, we present our experiments with multiple approaches, ranging from Ensemble of Learning to Deep Learning Networks on the weather modeling data to predict the rainfall. The competition was held on the online data science competition portal ‘Kaggle’. The results for weighted ensemble of learners gave us a top-10 ranking, with the testing root-mean-squared error being 0.5878.
A brief description of clustering, two relevant clustering algorithms(K-means and Fuzzy C-means), clustering validation, two inner validity indices(Dunn-n-Dunn and Devies Bouldin) .
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Need help with your statistics assignments? Our website offers top-notch statistics assignment help to students at all academic levels. Whether you're struggling with data analysis, hypothesis testing, or any other statistical concept, our experienced team of experts is here to assist you. We provide accurate solutions, clear explanations, and timely delivery to ensure your success. Our user-friendly platform makes it easy to submit your assignments and communicate with our dedicated professionals. Don't let statistics stress you out—visit our website today and let us handle your assignments with precision and expertise. Get the grades you deserve with our reliable statistics assignment help service!
Looking for professional assistance with your statistics assignments? Our website provides reliable “Statistics Assignment Help” services to help you with your assignment. Our experienced team of experts can provide personalized support and guidance, ensuring you understand the subject matter and achieve your academic goals. Contact us today to learn more about our services and how we can help you succeed.
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A brief description of clustering, two relevant clustering algorithms(K-means and Fuzzy C-means), clustering validation, two inner validity indices(Dunn-n-Dunn and Devies Bouldin) .
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
Exploring Support Vector Regression - Signals and Systems ProjectSurya Chandra
Our team competed in a Kaggle competition to predict the bike share use as a part of their capital bike share program in Washington DC using a powerful function approximation technique called support vector regression.
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing S...Pooyan Jamshidi
https://arxiv.org/abs/1606.06543
Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.
Continuous Architecting of Stream-Based SystemsCHOOSE
Pooyan Jamshidi CHOOSE Talk 2016-11-01
Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors making it a difficult task for software architects to refactor the initial designs. As an aid to designers and developers, we developed OSTIA (On-the-fly Static Topology Inference Analysis) that allows: (a) visualizing big data architectures for the purpose of design-time refactoring while maintaining constraints that would only be evaluated at later stages such as deployment and run-time; (b) detecting the occurrence of common anti-patterns across big data architectures; (c) exploiting software verification techniques on the elicited architectural models. In the lecture, OSTIA will be shown on three industrial-scale case studies.
See: http://www.choose.s-i.ch/events/jamshidi-2016/
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate
between algorithms with statistical implementation provides better consequence in terms of accurate
prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical
models, which provide less manual calculations. Presage is the essence of data science and machine
learning requisitions that impart control over situations. Implementation of any dogmas require proper
feature extraction which helps in the proper model building that assist in precision. This paper is
predominantly based on different statistical analysis which includes correlation significance and proper
categorical data distribution using feature engineering technique that unravel accuracy of different models
of machine learning algorithms.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Need help with your statistics assignments? Our website offers top-notch statistics assignment help to students at all academic levels. Whether you're struggling with data analysis, hypothesis testing, or any other statistical concept, our experienced team of experts is here to assist you. We provide accurate solutions, clear explanations, and timely delivery to ensure your success. Our user-friendly platform makes it easy to submit your assignments and communicate with our dedicated professionals. Don't let statistics stress you out—visit our website today and let us handle your assignments with precision and expertise. Get the grades you deserve with our reliable statistics assignment help service!
Looking for professional assistance with your statistics assignments? Our website provides reliable “Statistics Assignment Help” services to help you with your assignment. Our experienced team of experts can provide personalized support and guidance, ensuring you understand the subject matter and achieve your academic goals. Contact us today to learn more about our services and how we can help you succeed.
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The Data of an Observational Study Designed to Compare the Effectiveness of a...Statistics Assignment Help
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Pride Month Slides 2024 David Douglas School District
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2. 1. Which among the following techniques can be used to aid decision making when
those decisions depend upon some available data?
(a) descriptive statistics
(b) inferential statistics
(c) predictive analytics
(d) prescriptive analytics
Sol. (a), (b), (c), (d)
The techniques listed above allow us to visualise and understand data, infer properties
of data sets and compare data sets, as well as build models of the processes generating
the data in order to make predictions about unseen data. In different scenarios, each of
these processes can be used to help in decision making.
2. In a popular classification algorithm it is assumed that the entire input space can be
divided into axis-parallel rectangles with each such rectangle being assigned a class
label. This assumption is an example of
(a) language bias
(b) search bias
3. Sol. (a)
The mentioned constraints have to do with what forms the classification models can take
rather than the search process to find the specific model given some data. Hence, the
above assumption is an example of language bias.
3. Suppose we trained a supervised learning algorithm on some training data and observed
that the resultant model gave no error on the training data. Which among the following
conclusions can you draw in this scenario?
(a) the learned model has overfit the data
(b) it is possible that the learned model will generalise well to unseen data
(c) it is possible that the learned model will not generalise well to unseen data
(d) the learned model will definitely perform well on unseen data
(e) the learned model will definitely not perform well on unseen data
Sol. (b), (c)
Consider the (rare) situation where the given data is absolutely pristine, and our choice of
algorithm and parameter selection allow us to come up with a model which exactly
matches the process generating the data. In such a situation we can expect 100% accuracy
on the training data as well as good performance on unseen data.
4. where we create some synthetic data having a linear relationship between the inputs and
the output and then apply linear regression to model the data.
The more plausible situation is of course that that we used a very complex model which
ended up overfitting the training data. As we have seen, such models may achieve high
accuracy on the training data but generally perform poorly on unseen examples.
4. You are faced with a five class classification problem, with one class being the class
of interest, i.e., you care more about correctly classifying data points belonging to that
one class than the others. You are given a data set to use as training data. You analyse
the data and observe the following properties. Which among these properties do you
think are unfavourable from the point of view of applying general machine learning
techniques?
(a) all data points come from the same distribution
(b) there is class imbalance with much more data points belonging to the class of
interest than the other classes
(c) the given data set has some missing values
(d) each data point in the data set is independent of the other data points
5. Sol. (c)
Option (a) is favourable, since it is an implicit assumption we make when we try applying
supervised learning techniques. Option (b) indicates class imbalance which is usually an
issue when it comes to classification. However, note that the imbalance is in favour of the
class of interest. This means that there are a large number of examples for the class which
we are interested in, which should allow us to model this class well. Option (c) is of
course unfavourable as it would require us to handle missing data, and given the extent of
the data that is missing, could severely affect the performance of any classifier we come
up with. Finally, option (d) is favourable since, once again, it is an assumption about the
data that we implicitly make.
5. You are given the following four training instances:
• x1 = −1, y1 = 0.0319
• x2 = 0, y2 = 0.8692
• x3 = 1, y3 = 1.9566
• x4 = 2, y = 3.0343
Modelling this data using the ordinary least squares regression form of y = f(x) = b0 +
b1x, which of the following parameters (b0, b1) would you use to best model this data?
6. (a) (1,1)
(b) (1,2)
(c) (2,1)
Let us consider the model with parameters (1,1). We can find the sum of squared errors
using this parameter setting as
Sol. (a)
(0 − 0.319)2 + (1 − 0.8692)2 + (2 − 1.9566)2 + (3 − 3.0343)2 = 0.12
Repeating the same calculation for the other 3 options, we find that the minimum
squared error is obtained when the parameters are (1,1). Thus, this is the most suitable
parameter setting for modelling the given data.
6. You are given the following five training instances:
• x1 = 1, y1 = 3.4
• x2 = 1.5, y2 = 4.7
• x3 = 2, y3 = 6.15
• x4 = 2.25, y4 = 6.4
7. • x5 = 4, y5 = 10.9
Using the derivation results for the parameters in ordinary least squares, calculate the
values of b0 and b1. (Note that in the expression for b1, the last term in the denominator
is
(a) b0 = 7.54, b1 = 0.57
(b) b0 = 2.484, b1 = 0.969
(c) b0 = 0.969, b1 = 2.484
(d) b0 = 1, b1 = 2.5
Sol. (c)
According to the derivations we saw in the lectures, we have
and
8. Using the supplied data, we have:
N = 5
Using the above calculations, we have
9. Using this value of b1, we have
7. Recall the regression output obtained when using Microsoft Excel. In the third table,
there are columns for t Stat and P-value. Is the probability value shown here for a one-
sided test or a two-sided test?
(a) one-sided test
(b) two-sided test
Sol. (b)
10. Recall that the null hypothesis used here is H0: bi = 0, where bi is the coefficient
corresponding to the ith variable (i > 0), and b0 is the intercept.
8. In building a linear regression model for a particular data set, you observe the
coefficient of one of the features having a relatively high negative value. This suggests
that
(a) this feature has a strong effect on the model (should be retained)
(b) this feature does not have a strong effect on the model (should be ignored)
(c) it is not possible to comment on the importance of this feature without additional
information
Sol. (c)
A high magnitude suggests that the feature is important. However, it may be the case
that another feature is highly correlated with this feature and it’s coefficient also has a
high magnitude with the opposite sign, in effect cancelling out the effect of the former.
Thus, we cannot really remark on the importance of a feature just because it’s
coefficient has a relatively large magnitude.
11. 9. Assuming that for a specific problem, both linear regression and regression using the K-
NN approach give same levels of performance, which technique would you prefer if
response time, i.e., the time taken to make a prediction given an input data point, is a
major consideration?
(a) linear regression
(b) K-NN
(c) both are equally suitable
Sol. (a)
As we have seen, in K-NN, for each input data point for which we want to make a
prediction, we have to search the entire training data set for the neighbours of that point.
Depending upon the dimensionality of the data as well as the size of the training set, this
process can take some time. On the other hand in linear regression, once the model has
been learned we need to perform a single simple calculation to make a prediction for a
given data point.
10. You are given the following five training instances:
12. • x1 = 2, x2 = 1, y = 4
• x1 = 6, x2 = 3, y = 2
• x1 = 2, x2 = 5, y = 2
• x1 = 6, x2 = 7, y = 3
• x1 = 10, x2 = 7, y = 3
Using the K-nearest neighbour technique for performing regression, what will be the
predicted y value corresponding to the input point (x1 = 3, x2 = 6), for K = 2 and for K =
3?
(a) K = 2, y = 3; K = 3, y = 2.33
(b) K = 2, y = 3; K = 3, y = 2.66
(c) K = 2, y = 2.5; K = 3, y = 2.33
(d) K = 2, y = 2.5; K = 3, y = 2.66
Sol. (c)
When K = 2, the nearest points are x1 = 2, x2 = 5 and x1 = 6, x2 = 7. Taking the average
of the outputs of these two points, we have y = (2 + 3)/2 = 2.5.
13. Similarly, when K = 3, we additionally consider the point x1 = 6, x2 = 3 to get output, y
= (2 + 3 + 2)/3 = 2.33.
Weka-based programming assignment questions
The following questions are based on using Weka. Go through the tutorial on Weka
before attempting these questions. You can download the data sets used in this assignment
here.
Data set 1
This is a synthetic data set to get you started with Weka. This data set contains 100 data
points. The input is 3-dimensional (x1, x2, x3) with one output variable (y). This data is
in the arff format which can directly be used in Weka.
Tasks
For this data set, you will need to apply linear regression with regularisation, attribute
selection and collinear attribute elimination disabled (other parameters to be left to their
default values). Use 10-fold cross validation for evaluation.
14. Data set 2
This is a modified version of the prostate cancer data set from the ESL text book in the
arff format. It contains nine numeric attributes with the attribute lpsa being treated as the
target attribute. This data set is provided in two files. Use the test file provided for
evaluation (rather than the cross-validation method).
Tasks
For this data set, you will need to apply linear regression. First apply linear regression
with regularisation, attribute selection and collinear attribute elimination disabled. Next
enable regularisation and try out different values of the parameter and observe whether
better performance can be obtained with suitable values of the regularisation parameter.
Note that to apply regularisation you should normalise the data (except the target
variable). First normalise the test set and save the normalised data. Next open the training
data, normalise it and then run the regression algorithm (with the normalised test set
supplied for evaluation purposes).
Data set 3
15. This is the Parkinsons Telemonitoring data set taken from the UCI machine learning
repository. Given are two files, one for training and one for testing. The files are in csv
format and need to be converted into the arff format before algorithms are applied to it.
The last variable, PPE, is the target variable.
Tasks
For this data set, first apply linear regression with regularisation, attribute selection and
collinear attribute elimination disabled. Note the performance and compare with the
performance obtained by applying K-nearest neighbour regression. To run K-NN, select
the function IBk under the lazy folder. Leave all parameters set to their default values,
except for the K parameter (KNN in the interface).
11. What is the best linear fit for data set 1?
(a) y = 5*x1 + 6*x2 + 4*x3 + 12
(b) y = 3*x1 + 7*x2 - 2.5*x3 - 16
(c) y = 3*x1 + 7*x2 + 2.5*x3 - 16
(d) y = 2.5*x1 + 7*x2 + 4*x3 + 16
16. Sol. (b)
The data set used here is generated using the function specified by option (b) and with no
noise added, hence running linear regression on this data set should allow us to recover
the exact parameters and observe 100% accuracy.
12. Which of the following ridge regression parameter values leads to the lowest root
mean squared error for the prostate cancer data (data set 2) on the supplied test set?
(a) 0
(b) 2
(c) 4
(d) 8
(e) 16
(f) 32
Sol. (e)
Among the given choices of the ridge regression parameter, we observe best
performance (lowest error) at the value of 16.
17. 13. If a curve is plotted with the error on the y-axis and the ridge regression parameter on
the x-axis, then based on your observations in the previous question, which of the
following most closely resembles the curve?
(a) straight line passing through origin
(b) concave downwards function
(c) concave upwards function
(d) line parallel to x-axis
Sol. (c)
In general, as the ridge regression parameter is increased, the error begins to decrease,
but after a certain point, further increase in the parameter drives up the error.
14. Considering data set 3, is the performance of K-nearest neighbour regression (where
the value of the parameter K is varied between 1 and 25) comparable to the performance
of linear regression (without regularisation)?
(a) no
(b) yes
Sol. (b)
At K = 5, the performance is even better.