Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release wherein ROC-AUC curve supports Multi Class Classification. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.
To access our previous tutorial on the plotting of the ROC curve, click here.
The ROC-AUC score function can also be used in multi-class classification. Two averaging strategies are currently supported: the one-vs-one (OvO) algorithm computes the average of the pairwise ROC AUC scores and the one-vs-rest (OvR) algorithm computes the average of the ROC AUC scores for each class against all other classes.
In both cases, the multiclass ROC AUC scores are computed from probability estimates that a sample belongs to a particular class according to the model. The OvO and OvR algorithms support weighting uniformly (average=’macro’) and weighting by prevalence (average=’weighted’).
To begin with, we import multi classification, SVC and roc_auc_score. Then we specify the number of classes we want in the multi-classification function. Then we apply the SVC function and finally the roc_auc_score one. This function will give us the probable prediction for all the classes and we will then choose the one that has the highest probability. When we run it we get a ROC_AUC score of 0.99.
The code sheet is provided in a Github repository here.
Artificial Intelligence is the key to the future of weather forecasting, a fact well known. But did you know it is also powering earthquake prediction the world over? Yes. Artificial Intelligence techniques like machine learning are gradually being enlisted in forecasting seismic activity.
While earthquake prediction has not yet become an exact science, efforts are on to make improvements and make forecasts reliable. For this, AI powered neural networks, the same technology behind the success of driverless cars and digital assistants, is being used to enhance research based on seismic data.
A report says that, “Scientists say seismic data is remarkably similar to the audio data that companies like Google and Amazon use in training neural networks to recognize spoken commands on coffee-table digital assistants like Alexa.”
When it comes to studying earthquakes, it is the computer, a fast and able machine, looking for patterns in mountains of data rather than relying on the weary eyes of a scientist. Also, instead of a sequence of words, what the computer is studying is a sequence of ground-motion measurements.
Scientists in the US have experimented with neural networks to accelerate earthquake analysis and the speed at which they were producing results and studies was 500 times faster than they could in the past. Also, AI is not only useful in studying earthquakes but it is being used in forecasting earthquake aftershocks as well.
In fact, researchers say it is a time of great scientific advancement, so much so, that “technology can do as well as — or better than — human experts”.
Geophysicist Paul Johnson’s team in the US has been studying earthquakes for quite some time now and it has made advancements in “using pattern-finding algorithms similar to those behind recent advances in image and speech recognition and other forms of artificial intelligence, (where) he and his collaborators successfully predicted temblors in a model laboratory system — a feat that has since been duplicated by researchers in Europe”, says a report.
Now Mr Johnson’s team has published a paper wherein artificial intelligence has been used to study slow slip earthquakes in the Pacific Northwest. While advancements are being made in the field of studying slow slip earthquakes, it is the bigger and more potent ones that really need to be studied. But they are rare. So the question remains – Will Machine Learning be able to analyse a small data set and predict with confidence the next big earthquake?
Researchers claim “that their (machine learning) algorithms won’t actually need to train on catastrophic earthquakes to predict them.” Studies conducted recently suggest “seismic patterns before small earthquakes are statistically similar to those of their larger counterparts”. So, a computer trained on hundreds and thousands of those small temblors might be able enough to predict the big ones.
Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. We, through this video tutorial, aim to learn about the much talked about new release called Plotting API. Prior to this version, Scikit-learn did not have a function to plot the ROC curve.
A new plotting API is available for creating visualizations. The new API allows for quickly adjusting the visuals of a plot without involving any recomputation. It is also possible to add different plots to the same figure. In this tutorial we are going to study the plotting of the ROC curve.
The code sheet is provided in a Github repository here.
We will attempt to plot the ROC curve on two different algorithms and compare which one is a better function. First we choose to make a classification data. Then we go on to plot the ROC curve using SVC classifier and then further plot the curve using a random forest classifier.
Even as the coronavirus pandemic rages on and India is living through a strict lockdown to abate the spread of the novel virus, a disastrous spell of a plague of crop destroying locusts has struck Rajasthan, Gujarat and parts of Madhya Pradesh.
Threatening to balloon into an agrarian crisis, the destruction of crops on this scale is being seen as one “worst in decades”. In fact, such large scale breeding of locusts and an attack by them is the worst in 27 years, government officials said.
In such frightening circumstances, what we can truly bank upon to detect and fight locust attacks is advanced technology like machine learning techniques. This essay aims to demystify how machine learning can be used to detect locust breeding patterns by studying soil moisture through remote sensing.
A study called “Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture” shows how researchers have “used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM (Soil Moisture) conditions under different time scenarios…It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m3 / m3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA (European Space Agency) CCI (Climate Change Initiative) SM product to complement or substitute current monitoring techniques based on precipitation datasets.”
The study found that “it is widely assumed” that rainfall over 25 mm in two consecutive months is conducive to locust breeding. Likewise, various soil moisture conditions affect breeding patterns greatly. So, the study finds that it is important to have “variable creation as a previous step to modeling”. Different time intervals of locust breeding were tested by the researchers for model creation. Also, different soil moisture values were considered.
It was found that the “highest performance was acquired by the RF (Random Forest) algorithm when dividing the whole survey time into ranges of 6 days, and selecting the minimum SM as the variable value.” GLMs of Generalised Linear Models, however, did not work well according to the study.
The applied methodology of machine learning offers promising results to accurately identify breeding areas based on data pertaining to 30 years of SM values. The ESA CCI soil moisture data is one of the most authoritative ones in the world. Thus the researchers who conducted this study are confident that their results signify a breakthrough in locust monitoring technique prevalence in the world.
This study, thus, proposes a machine learning approach based on SM time series “to predict breeding areas, by means of remote sensing”. Artificial Intelligence and Machine Learning will help future researchers and scientists to study and produce better warning systems based on the results of this study. In this study only soil moisture data has been used but more variables like temperatures can also be taken into account to accurately predict breeding grounds in the future.
The Covid-19 pandemic has struck India like it has scores of countries across the world. As of May 27, over 1,51,000 Indians have been tested positive for the novel virus and over 4000 people have died due to the contagious disease. India has been under lockdown for over two months now in an attempt at abating the spread of the virus due to movement and contact.
With all offices closed and work from home decreed across numerous sectors of the economy, professionals have been forced to adapt to a new mode of work and training. With more time on hand since they are working from home, professionals are upgrading their skills by taking up online training modules and classes. A recent LinkedIn survey throws light on this phenomenon.
LinkedIn’s Work Force Confidence Index
India’s foremost social networking site that helps individuals network with professional peers and find jobs and appointments has conducted a survey called Work Force Confidence Index. As per the survey conducted between April 27 and May 3, “India’s professionals are logging learning hours for not just knowledge acquisition but also to increase productivity. About half of respondents from mid-market firms joined courses that help them manage time better, improve prioritisation or stay organised”.
93% respondents to upskill online in next two weeks
According to LinkedIn News India, 1040 professionals were surveyed by LinkedIn and 93% of them said “their time spent on e-learning will either increase or remain the same over the next two weeks”. Moreover, 60% of the respondents of which 74% were from the engineering domain said e-learning was a conduit to furthering industry knowledge. “Advancing in one’s career was a driver for 57% of all respondents and 3 in 10 active job seekers undertook e-learning to make a career pivot,” said LinkedIn News India.
What respondents learnt
Of the respondents, 45% said they hoped to learn to collaborate with peers through online learning in lockdown. Also, 43% said they wished to learn to manage time and prioritise and stay organised. Moreover, 40% said they hoped to learn something unrelated to work through online platforms. Becoming a leader and managing personal finances were pegged at 37% and 32% respectively by the study as goals and 24% said e-learning could actually lead to a change in career paths for them.
Advantages of e-learning
Travelling to work and back is taxing and time consuming. When you are working from home, you save on energy and time that can be used for something productive like e-learning training modules. They are easy on the pocket, accessible from absolutely anywhere you are and convenient to absorb and retain information and new things learnt. Moreover, there is a large online community to help you out with study material and guidance.
The world has seen a transformation in its economic activities since the coronavirus pandemic broke out. Economies have come to a grinding halt and manufacturing has dipped. Now what nations need is resilience and strength to carry on production in all sectors. What they are most depending on is the power of Artificial Intelligence to enhance the manufacturing process and help save money and drive down costs.
Here are some examples of how AI is powering the manufacturing sector in 2020.
AI is being used to transform machinery maintenance and quality in manufacturing operations today, according to Capgemini.
Caterpillar’s Marine Division is using machine learning to analyze data on how often its shipping equipment should be cleaned helping it save thousands of dollars.
The BMW Group is using AI to study manufacturing component images in and spot deviations from the standard production procedure in real-time.
In fact, a study shows that in the four earlier global economic downturns companies using AI were actually successful in increasing both sales and profit margins. Companies are all striving to utilize human experience, insights and AI techniques to give manufacturing a fillip in these times of a crisis.
Manufacturing using AI in real-time
Real-time monitoring of the manufacturing process is advantageous because it translates to sorting out production bottlenecks, tracking scrap rates and meeting customer deadlines among other things. The huge cache of data used can be utilized to build machine learning models.
Supervised and unsupervised machine learning algorithms can study multiple production shifts’ real-time data within seconds and predict processes, products, and workflow patterns that were not known before. A report suggests 29% of AI implementations in manufacturing are for maintaining machinery and production assets.
It was found that the most popular use of AI in manufacturing is predicting when equipment are likely to fail and suggesting optimal times to conduct maintenance. Companies like General Motors analyze images of its robots from cameras mounted above to spot anomalies and possible failures in the production line and thus preempt outages.
General Motors uses AI algorithms to give and produce optimized product design. General Motors can achieve the goal of rapid prototyping with the help of AI and ML algorithms. Designers provide definitions of the functional needs, raw materials, manufacturing methods and other constraints and the company along with AutoDesk has customized Dreamcatcher to optimize for weight and other vital criterion. In this way, AI comes together with human endeavor to produce a-class product designs that cost lesser.
Nokia has begun using a video application that takes the help of machine learning to alert an assembly operator if there are inconsistencies in the production process in one of its factories in Oulu, Finland. It alerts a machine operator about inconsistencies in the production of electronic items and this helps preempt poor production process and helps the company save on a lot of money and capital.
There are many other production processes AI is helping revolutionize. Only time will tell how much of AI will power the manufacturing sector. But this technological advancement is surely making an impact on economies worldwide. Meanwhile, for more details, do peruse the DexLab Analytics website. DexLab Analytics is a premiere machine learning institute in Gurgaon.
Today we are going to learn about the new releases from Scikit-learn version 0.22, a machine learning library in Python. First we learn how to install it on our systems. Then, we come to the much talked about new release called stacking regression.
Now, how does stacking regression work? Well, you have been using machine learning algorithms like Decision Tree or Random Forest. Have you heard of Voter Classifier? It is an algorithm in Scikit-learn. Ensemble algorithm is a combination of two or more algorithms to make it stronger.
When working on a set of data, we must apply all these algorithms to get predicted values. Then we vote out classified predicted values in Voter Classifier. Stacking Classifier is different. What we are doing in it is stacking together the predicted values to make a new input.
Initially, we make prediction by using various algorithms separately. Their results or output are then concatenated together. Then we use this output as a new input and apply the algorithms to it to get target variable. This method is known as stacking regression.
We try this out on a data set that can be taken from a github repository the link to which is given below.
Then we use two algorithms as estimators. Then we use stacking regression to build a model. For more on this do watch the video attached herewith. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.
Chatbots or “conversational agents” are software applications that mimic or imitate written or spoken human speech for the purposes of facilitating a conversation or interaction with a human being.
These applications have become one of the most ubiquitous software applications out there with the advancement of machine learning technology and NLP.
“Today’s chatbots are smarter, more responsive, and more useful – and we’re likely to see even more of them in the coming years… chatbots are used most commonly in the customer service space, assuming roles traditionally performed by living, breathing human beings such as Tier-1 support operatives and customer satisfaction reps.”
Conversational agents are becoming a common occurrence partly due to the fact that barriers to entry in creating chatbots such as sophisticated programming knowledge have become redundant.
How Chatbots work
The crux of chatbot technology is natural language processing or NLP, the same technology “that forms the basis of the voice recognition systems used by virtual assistants such as Google Now, Apple’s Siri, and Microsoft’s Cortana.” “Chatbots process the text presented to them by the user…infer what they mean and/or want, and determine a series of appropriate responses based on this information.”
Here are 5 companies using chatbots for various roles like marketing, communicating with marginalized groups and patients suffering from sleeplessness and memory loss.
Russian technology company Endurance developed a companion chatbot to help dementia patients cope with decreased verbal ability. Many patients with Alzheimer’s disease use the chatbot to converse with. In turn, the chatbot identifies deviations in conversational patterns of the patient that might indicate a problem with memory and recollection.
Casper’s Insomnobot 3000 is a conversational agent that aims to help insomniacs by posing as a companion to talk to while the rest of the world sleeps. However, at this point, “Insomnobot 3000 is a little rudimentary.”
International child advocacy nonprofit UNICEF is using chatbots to help people living in developing countries speak out about the most urgent needs in their communities. The bot, named U-Report, focuses on large-scale data gathering via polls. UNICEF then uses feedback as the basis for potential policy recommendations.
This chatbot aims at making medical diagnoses faster, easier, and more transparent for both patients and physicians. MedWhat is powered by a highly sophisticated machine learning system that offers increasingly accurate responses to user questions based on behaviors that it “learns” by interacting with human beings. Also, it acts as a repository of a vast source of medical journals and medical advice.
Roof Ai is a chatbot that helps real-estate marketers to “automate interacting with potential leads and lead assignment via social media”. The bot identifies potential leads via social media and responds immediately, irrespective of the time of the day. “Based on user input, Roof Ai prompts potential leads to provide a little more information, before automatically assigning the lead to a sales agent.”
Machine Learning is an acquired knowledge science. It has to be taught and studied. For this, it is imperative to have the best books on the subject at hand. However, most books on the subject are expensive and not easily accessible. This is only fair given the amount of hard word that goes into writing these books.
In a situation this critical, it is best to rely on the good old Internet for assistance. There are some good Samaritans who have chosen to make their works freely available to all. Here is a great guide to free ebooks available online so you can brush up on your concepts and be industry ready at the earliest.
Think Stats – Probability and Statistics for Programmers by Allen B Downey
This is an introduction to statistics and probability for those who have a basic grounding in Python programming. “It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs,” says a report.
Bayesian Reasoning and Machine Learning by David Barber
When it comes to Bayesian statistics, this book is a classic. “This takes a Bayesian statistics approach to machine learning.”This is a book worth checking out for anyone getting into the machine learning field and trying to make a career out of the subject.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This popular entry is an introduction to data science through machine learning. “This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It’s filled with practical real-world examples of where and how algorithms work. For those with an inclination towards R programming, this book even has practical examples in R.”
Understanding Machine Learning by ShaiShalev-Shwartz and Shai Ben-David
“This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including…stochastic gradient descent, neural networks, and structured output learning.”
A Programmer’s Guide to Data Mining by Ron Zacharski
This book has chapters covering recommendation systems. “It takes a…visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured Text and how to deal with it, in case you are thinking about getting into Natural Language Processing. Examples in Python are also available in case you want to practice.”