To do that, he and his team collected thousands of geotagged posts tweeted between July 2013 and July 2014 in New York state, and then winnowed them down to tweets containing booze-related keywords (ranging from "beer keg" to "shitfaced"). Each tweet passed through three human "Turkers," who were asked three questions: Q1: Does the tweet make any reference to drinking alcoholic beverages? Q3: if so, is it likely that the tweet was sent at the time and place the tweeter was drinking alcoholic beverages? The success rate--that is, the rate at which the machines' answers matched the Turkers' consensus--ranged from 92 percent for the algorithm answering Q1, to 82 percent for the drunk-spotting algorithm answering Q3.

Originally published at innoarchitech.com here on March 18, 2016. Welcome to the fifth and final chapter in a five-part series about machine learning. In this final chapter, we will revisit unsupervised learning in greater depth, briefly discuss other fields related to machine learning, and finish the series with some examples of real-world machine learning applications. Recall that unsupervised learning involves learning from data, but without the goal of prediction. This is because the data is either not given with a target response variable (label), or one chooses not to designate a response.

Linear regression is arguably one of the most widely used techniques in the data science world. But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired. First, a little history, the term regression was first used by Sir Francis Galton, a 19th century polymath. Galton was a pioneer in application of statistical methods in many branches of science, he studied the relative sizes of parents and their offsprings in various species of plants and animals. During this study he observed that a larger than average parent tends to produce a larger than average child, but the child is likely to be less large than the parent in terms of its relative position in its own generation.

Welcome to the fifth and final chapter in a five-part series about machine learning. In this final chapter, we will revisit unsupervised learning in greater depth, briefly discuss other fields related to machine learning, and finish the series with some examples of real-world machine learning applications. Recall that unsupervised learning involves learning from data, but without the goal of prediction. This is because the data is either not given with a target response variable (label), or one chooses not to designate a response. It can also be used as a pre-processing step for supervised learning.

This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learning different types of data. Three of these packages include Support Vector Machines (SVM) [1], Generalized Linear Models (GLM) [2], and Adaptive Boosting (AdaBoost) [3]. While all three packages can be highly accurate for various types of classification problems, each package performs very differently when modeling (i.e. In particular, model fitting for Generalized Linear Models execute in much shorter periods of time than either Support Vector Machines or Adaptive Boosting.

"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system.

When the k-means clustering algorithm runs, it uses a randomly generated seed to determine the starting centroids of the clusters. However, if the data is evenly distributed, then we might end up with different cluster members based on the initial random variable. An example for such a behavior is shown. R is used for the experiment. The code to load the data and the contents of the data are as follows. We try to group the samples based on two feature variables - age and bmi.

This is part 2/3 of a series on deep learning and deep belief networks. This section will focus on artificial neural networks (ANNs) by building upon the logistic regression model we learned about last time. It'll be a little shorter because we already built the foundation for some very important topics in part 1 – namely the objective / error function and gradient descent. We will focus on 2 main functions of ANNs – the forward pass (prediction) and backpropagation (learning). Your sci-kit learn analogues would be model.predict()

There are several reasons why everyone isn't using Bayesian methods for regression modeling. One reason is that Bayesian modeling requires more thought: you need pesky things like priors, and you can't assume that if a procedure runs without throwing an error that the answers are valid. A second reason is that MCMC sampling -- the bedrock of practical Bayesian modeling -- can be slow compared to closed-form or MLE procedures. A third reason is that existing Bayesian solutions have either been highly-specialized (and thus inflexible), or have required knowing how to use a generalized tool like BUGS, JAGS, or Stan. This third reason has recently been shattered in the R world by not one but two packages: brms and rstanarm.

In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-$\chi^2$, on a wide range of publicly available datasets. The proposed fRBF kernel performs very similarly to the RBF kernel. Both RBF and fRBF kernels require an important tuning parameter ($\gamma$). Interestingly, for a significant portion of the datasets, the min-max kernel outperforms the best-tuned RBF/fRBF kernels. The acos kernel and acos-$\chi^2$ kernel also perform well in general and in some datasets achieve the best accuracies. One crucial issue with the use of nonlinear kernels is the excessive computational and memory cost. These days, one increasingly popular strategy is to linearize the kernels through various randomization algorithms. In our study, the randomization method for the min-max kernel demonstrates excellent performance compared to the randomization methods for other types of nonlinear kernels, measured in terms of the number of nonzero terms in the transformed dataset. Our study provides evidence for supporting the use of the min-max kernel and the corresponding randomized linearization method (i.e., the so-called "0-bit CWS"). Furthermore, the results motivate at least two directions for future research: (i) To develop new (and linearizable) nonlinear kernels for better accuracies; and (ii) To develop better linearization algorithms for improving the current linearization methods for the RBF kernel, the acos kernel, and the acos-$\chi^2$ kernel. One attempt is to combine the min-max kernel with the acos kernel or the acos-$\chi^2$ kernel. The advantages of these two new and tuning-free nonlinear kernels are demonstrated vias our extensive experiments.