AI News, Conjecture: Scalable Machine Learning in Hadoop with Scalding

Conjecture: Scalable Machine Learning in Hadoop with Scalding

For instance, we use predictive machine learning models to estimate click rates of items so that we can present high quality and relevant items to potential buyers on the site.

In addition to contributing to on-site experiences, we use machine learning as a component of many internal tools, such as routing and prioritizing our internal support e-mail queue.

for inbound support e-mails, we can assign support requests to the appropriate personnel and ensure that urgent requests are handled by staff more rapidly, helping to ensure a good customer experience.

To quickly develop these types of predictive models while making use of our MapReduce cluster, we decided to construct our own machine learning framework, open source under the name “Conjecture.”

 It consists of three main parts: This article is intended to give a brief introduction to predictive modeling, an overview of Conjecture’s capabilities, as well as a preview of what we are currently developing.

 For the classification of e-mails as urgent or not, we took the historic e-mails as the inputs, and an indicator of whether the support staff had marked the email as urgent or not after reading its contents.

The choice to store the names of features along with their numeric value allows us to easily inspect for any weirdness in our input, and quickly iterate on models by finding the causes of problematic predictions.

Conjecture, like many current libraries for performing scalable machine learning, leverages a family of techniques known as online learning. In online learning, the model processes the labeled training examples one at a time, making an update to the underlaying prediction function after each observation.

 While the online learning paradigm isn’t compatible with every machine learning technique, it is a natural fit for several important  classes of machine learning models such as logistic regression and large margin classification, both of which are implemented in Conjecture.

However, traditionally the online learning framework does not directly lend itself to a parallel method for model training. Recent research into parallelized online learning in Hadoop gives us a way to perform sequential updates of many models in parallel across many machines, each separate process consuming a fraction of the total available training data.

of the process for the purpose of further training. Theoretical results tell us, that when performed correctly, this process will result in a reliable predictive model, similar to what would be generated had there been no parallelization.

 The key to our distributed online machine learning algorithm is in the definition of appropriate reduce operations so that map-side aggregators will implement as much of the learning as possible.

 When the reduce operation is consuming two models which both have some training, they are merged (e.g., by summing or averaging the parameter values) otherwise, we continue to train whichever model has some training already.

 This leads to more flexibility in the types of models we can train, and also lends better statistical properties to the resulting models (for example by reducing variance in the gradient estimates in the case of logistic regression).

 Since this procedure yields one set of performance metrics for each fold, we take the appropriately weighted means of these, and the observed variance also gives an indication of the reliability of the estimate.

 On the other hand if there is a great discrepancy between the performance on different folds then it suggests that the mean will be an unreliable estimate of future performance, and possibly that either more data is needed or the model needs some more feature engineering.

To accomplish this, we deploy our model file (a single JSON string encoding the internal model data structures) to the servers where we instantiate it using PHP’s json_decode() function.

An example of a json-encoded conjecture model is below: Currently the JSON-serialized models are stored in a git repository and deployed to web servers via Deployinator (see the Code as Craft post about it here).

Deployinator broadcasts models to all the servers running code that could reference Conjecture models, including the web hosts, and our cluster of gearman workers that perform asynchronous tasks, as well as utility boxes which are used to run cron jobs and ad hoc jobs.

Machine learning

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to 'learn' (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.[2]

These analytical models allow researchers, data scientists, engineers, and analysts to 'produce reliable, repeatable decisions and results' and uncover 'hidden insights' through learning from historical relationships and trends in the data.[9]

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.'[10]

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.

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.[14]: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.

Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples).

The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.[16]

The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

An artificial neural network (ANN) learning algorithm, usually called 'neural network' (NN), is a learning algorithm that is vaguely inspired by biological neural networks.

They are usually used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown joint probability distribution between observed variables.

Falling hardware prices and the development of GPUs for personal use in the last few years have contributed to the development of the concept of deep learning which consists of multiple hidden layers in an artificial neural network.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.

Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.

Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters.

Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG).

Representation learning algorithms often attempt to preserve the information in their input but transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions, allowing reconstruction of the inputs coming from the unknown data generating distribution, while not being necessarily faithful for configurations that are implausible under that distribution.

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.

genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, and uses methods such as mutation and crossover to generate new genotype in the hope of finding good solutions to a given problem.

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%.

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.[38]

Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[45]

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.

In comparison, the N-fold-cross-validation method randomly splits the data in k subsets where the k-1 instances of the data are used to train the model while the kth instance is used to test the predictive ability of the training model.

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.[62][63]

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.[65]

Machine Learning

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.

Supervised learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.[1]

In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.

This requires the learning algorithm to generalize from the training data to unseen situations in a 'reasonable' way (see inductive bias).

There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem).

The prediction error of a learned classifier is related to the sum of the bias and the variance of the learning algorithm.[4]

But if the learning algorithm is too flexible, it will fit each training data set differently, and hence have high variance.

A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing a bias/variance parameter that the user can adjust).

The second issue is the amount of training data available relative to the complexity of the 'true' function (classifier or regression function).

If the true function is simple, then an 'inflexible' learning algorithm with high bias and low variance will be able to learn it from a small amount of data.

But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of the input space), then the function will only be learnable from a very large amount of training data and using a 'flexible' learning algorithm with low bias and high variance.

If the input feature vectors have very high dimension, the learning problem can be difficult even if the true function only depends on a small number of those features.

Hence, high input dimensionality typically requires tuning the classifier to have low variance and high bias.

In practice, if the engineer can manually remove irrelevant features from the input data, this is likely to improve the accuracy of the learned function.

In addition, there are many algorithms for feature selection that seek to identify the relevant features and discard the irrelevant ones.

This is an instance of the more general strategy of dimensionality reduction, which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm.

fourth issue is the degree of noise in the desired output values (the supervisory target variables).

If the desired output values are often incorrect (because of human error or sensor errors), then the learning algorithm should not attempt to find a function that exactly matches the training examples.

You can overfit even when there are no measurement errors (stochastic noise) if the function you are trying to learn is too complex for your learning model.

In such a situation, the part of the target function that cannot be modeled 'corrupts' your training data - this phenomenon has been called deterministic noise.

In practice, there are several approaches to alleviate noise in the output values such as early stopping to prevent overfitting as well as detecting and removing the noisy training examples prior to training the supervised learning algorithm.

There are several algorithms that identify noisy training examples and removing the suspected noisy training examples prior to training has decreased generalization error with statistical significance.[5][6]

When considering a new application, the engineer can compare multiple learning algorithms and experimentally determine which one works best on the problem at hand (see cross validation).

Given fixed resources, it is often better to spend more time collecting additional training data and more informative features than it is to spend extra time tuning the learning algorithms.

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For example, naive Bayes and linear discriminant analysis are joint probability models, whereas logistic regression is a conditional probability model.

In both cases, it is assumed that the training set consists of a sample of independent and identically distributed pairs,

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contains many candidate functions or the training set is not sufficiently large, empirical risk minimization leads to high variance and poor generalization.

The regularization penalty can be viewed as implementing a form of Occam's razor that prefers simpler functions over more complex ones.

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