AI News, Astonishing Hierarchy of Machine Learning Needs
- On Wednesday, June 6, 2018
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Astonishing Hierarchy of Machine Learning Needs
Machine Learning is hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need.
Although Machine Learning has now gained prominence owing to the exponential rate (1990 was the flying gear year) of data generation and technological advancements to support it but it has roots from old days.
Data science is more about the extraction of knowledge (KDD) from data through algorithms to answer particular question or solve particular problems In large-scale data available systems, intelligent systems with best suitable algorithm analyze, detect patterns and learn to inform decision and information. Machine learning helps data science by making a provision for data analysis, data preparation and even decision making. The word learning in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters.
Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.
Controlling machine-learning algorithms and their biases
This often-overlooked defect can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions.
Effective efforts to confront this problem at the outset will repay handsomely, allowing the true potential of machine learning to be realized most efficiently.
In the domain of artificial intelligence, machine learning increasingly refers to computer-aided decision making based on statistical algorithms generating data-driven insights (see sidebar, “Machine learning: The principal approach to realizing the promise of artificial intelligence”).
To create a functioning statistical algorithm by means of a logistic regression, for example, missing variables must be replaced by assumed numeric values (a process called imputation).
Machine learning is able to manage vast amounts of data and detect many more complex patterns within them, often attaining superior predictive power.
With access to the right data and guidance by subject-matter experts, predictive machine-learning models could find the hidden patterns in the data and correct for such spikes.
Confirmation bias is the tendency to select evidence that supports preconceived beliefs, while loss-aversion bias imposes undue conservatism on decision-making processes.
Machine learning is being used in many decisions with business implications, such as loan approvals in banking, and with personal implications, such as diagnostic decisions in hospital emergency rooms.
Where machine learning predicts behavioral outcomes, the necessary reliance on historical criteria will reinforce past biases, including stability bias.
Just as a traumatic childhood accident can cause lasting behavioral distortion in adults, so can unrepresentative events cause machine-learning algorithms to go off course.
Should a series of extraordinary weather events or fraudulent actions trigger spikes in default rates, for example, credit scorecards could brand a region as “high risk”
Companies seeking to overcome biases with statistical decision-making processes may find that the data scientists supervising their machine-learning algorithms are subject to these same biases.
It is frustratingly difficult to shape machine-learning algorithms to recognize a pattern that is not present in the data, even one that human analysts know is likely to manifest at some point.
Since machine-learning algorithms try to capture patterns at a very detailed level, however, every attribute of each synthetic data point would have to be crafted with utmost care.
In 2007, an economist with an inkling that credit-card defaults and home prices were linked would have been unable to build a predictive model showing this relationship, since it had not yet appeared in the data.
As described in a previous article in McKinsey on Risk, companies can take measures to eliminate bias or protect against its damaging effects in human decision making.
First, users of machine-learning algorithms need to understand an algorithm’s shortcomings and refrain from asking questions whose answers will be invalidated by algorithmic bias.
They must understand the true values involved in the trade-off: algorithms offer speed and convenience, while manually crafted models, such as decision trees or logistic regression—or for that matter even human decision making—are approaches that have more flexibility and transparency.
Health-conscious consumers must study literature on nutrition and read labels in order to avoid excess calories, harmful additives, or dangerous allergens.
In credit scoring, for example, built-in stability bias prevents machine-learning algorithms from accounting for certain rapid behavioral shifts in applicants.
Burdened by an exceptionally high monthly installment (due to the short tenor), many of these applicants will ultimately default, causing a spike in credit losses.
Should business users fail to recognize these shifts, banks might be able to identify them indirectly, by monitoring the distribution of monthly applications by loan tenor.
The challenge here is to establish whether a marked shift is due to a deliberate change in behavior by applicants or to other factors, such as changes in economic conditions or a bank’s promotional strategy.
Tests can ensure that unwanted biases of past human decision makers, such as gender biases, for example, have not been inadvertently baked into machine-learning algorithms.
Experts with deep machine-learning knowledge and good business judgment are like experienced gardeners, carefully nurturing the plants to encourage their organic growth.
By using stratified sampling and optimized observation weights, data scientists ensure that the algorithm is most powerful for those decisions in which the business impact of a prediction error is the greatest.
Traditional approaches include human decision making or handcrafted models such as decision trees or logistic-regression models—the analytic workhorses used for decades in business and the public sector to assign probabilities to outcomes.
Three questions can be considered when deciding to use machine-learning algorithms: In addition to these considerations, companies implementing large-scale machine-learning programs should make appropriate organizational and cultural changes to support them.
While not as stringent and formal, the approach is related to mature model development and validation processes by which large institutions are gaining strategic control of model proliferation and risk.
Three building blocks are critically important for implementation: Creating a conscious, standards-based system for developing machine-learning algorithms will involve leaders in many judgment-based decisions.
exercise designed to pinpoint the limitations of a proposed model and help executives judge the business risks involved in a new algorithm.
Essentials of Machine Learning Algorithms (with Python and R Codes)
Note: This article was originally published on Aug 10, 2015 and updated on Sept 9th, 2017
Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.
The idea behind creating this guide is to simplify the journey of aspiring data scientists and machine learning enthusiasts across the world.
How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).
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.
This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
These algorithms can be applied to almost any data problem: It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s).
In this equation: These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line.
And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding best fit line, you can fit a polynomial or curvilinear regression.
It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s).
It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression).
source: statsexchange In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ‘if they will play or not’.
In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.
For example, if we only had two features like Height and Hair length of an individual, we’d first plot these two variables in two dimensional space where each point has two co-ordinates (these co-ordinates are known as Support Vectors)
In the example shown above, the line which splits the data into two differently classified groups is the black line, since the two closest points are the farthest apart from the line.
It is a classification technique based on Bayes’ theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Step 1: Convert the data set to frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.
Yes) * P(Yes) / P (Sunny) Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64 Now, P (Yes |
However, it is more widely used in classification problems in the industry. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors.
Its procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters).
We know that as the number of cluster increases, this value keeps on decreasing but if you plot the result you may see that the sum of squared distance decreases sharply up to some value of k, and then much more slowly after that.
grown as follows: For more details on this algorithm, comparing with decision tree and tuning model parameters, I would suggest you to read these articles: Python R Code
For example: E-commerce companies are capturing more details about customer like their demographics, web crawling history, what they like or dislike, purchase history, feedback and many others to give them personalized attention more than your nearest grocery shopkeeper.
How’d you identify highly significant variable(s) out 1000 or 2000? In such cases, dimensionality reduction algorithm helps us along with various other algorithms like Decision Tree, Random Forest, PCA, Factor Analysis, Identify based on correlation matrix, missing value ratio and others.
GBM is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. Boosting is actually an ensemble of learning algorithms which combines the prediction of several base estimators in order to improve robustness over a single estimator.
The XGBoost has an immensely high predictive power which makes it the best choice for accuracy in events as it possesses both linear model and the tree learning algorithm, making the algorithm almost 10x faster than existing gradient booster techniques.
It is designed to be distributed and efficient with the following advantages: The framework is a fast and high-performance gradient boosting one based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Since the LightGBM is based on decision tree algorithms, it splits the tree leaf wise with the best fit whereas other boosting algorithms split the tree depth wise or level wise rather than leaf-wise.
So when growing on the same leaf in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm and hence results in much better accuracy which can rarely be achieved by any of the existing boosting algorithms.
Catboost can automatically deal with categorical variables without showing the type conversion error, which helps you to focus on tuning your model better rather than sorting out trivial errors.
My sole intention behind writing this article and providing the codes in R and Python is to get you started right away. If you are keen to master machine learning, start right away.
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to 'learn' (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs.
Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.' This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.
Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning 'signal' or 'feedback' available to a learning system: Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system::3 Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience.
Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.
Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.:708–710;
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.
Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.
Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.
In machine learning, genetic algorithms found some uses in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms. Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply, knowledge.
They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. Applications for machine learning include: In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.
A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of Machine Learning to predict the financial crisis.
 In 2012, co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it has been reported that a machine learning algorithm has been applied in Art History to study fine art paintings, and that it may have revealed previously unrecognized influences between artists. Although machine learning has been very transformative in some fields, effective machine learning is difficult because finding patterns is hard and often not enough training data are available;
as a result, machine-learning programs often fail to deliver. Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.
Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices. For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants. Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.
There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed. Software suites containing a variety of machine learning algorithms include the following :
Difference between Machine Learning & Statistical Modeling
One of the most common question, which gets asked at various data science forums is: What is the difference between Machine Learning and Statistical modeling?
When I came across this question at first, I found almost no clear answer which can layout how machine learning is different from statistical modeling.
Given the similarity in terms of the objective both try to solve for, the only difference lies in the volume of data involved and human involvement for building a model.
Here is an interesting Venn diagram on the coverage of machine learning and statistical modeling in the universe of data science (Reference: SAS institute)
The common objective behind using either of the tools is Learning from Data. Both these approaches aim to learn about the underlying phenomena by using data generated in the process.
Let us now see an interesting example published by McKinsey differentiating the two algorithms : Case : Understand the risk level of customers churn over a period of time for a Telecom company Data Available : Two Drivers –
Even with a laptop of 16 GB RAM I daily work on datasets of millions of rows with thousands of parameter and build an entire model in not more than 30 minutes.
Given the flavor of difference in output of these two approaches, let us understand the difference in the two paradigms, even though both do almost similar job : All the differences mentioned above do separate the two to some extent, but there is no hard boundary between Machine Learning and statistical modeling.
subfield of computer science and artificial intelligence which deals with building systems that can learn from data, instead of explicitly programmed instructions.
It came into existence in the 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so.
The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.
In a statistical model, we basically try to estimate the function f in Machine Learning takes away the deterministic function “f”
It simply becomes It will try to find pockets of X in n dimensions (where n is the number of attributes), where occurrence of Y is significantly different.
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