In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost tree based classifier to obtain high scored classification. Using the generalized linear model for logistic regression makes it possible to analyze the influence of the factors under study. The Bayesian approach for logistic regression gives the statistical distribution for the parameters of the model. It can be useful in the probabilistic analysis, e.g. risk assessment.

Whitening, or sphering, is a common preprocessing step in statistical analysis to transform random variables to orthogonality. However, due to rotational freedom there are infinitely many possible whitening procedures. Consequently, there is a diverse range of sphering methods in use, for example based on principal component analysis (PCA), Cholesky matrix decomposition and zero-phase component analysis (ZCA), among others. Here we provide an overview of the underlying theory and discuss five natural whitening procedures. Subsequently, we demonstrate that investigating the cross-covariance and the cross-correlation matrix between sphered and original variables allows to break the rotational invariance and to identify optimal whitening transformations. As a result we recommend two particular approaches: ZCA-cor whitening to produce sphered variables that are maximally similar to the original variables, and PCA-cor whitening to obtain sphered variables that maximally compress the original variables.

Among the many R packages, there is the outbreaks package. It contains datasets on epidemics, on of which is from the 2013 outbreak of influenza A H7N9 in China, as analysed by Kucharski et al. (2014): I will be using their data as an example to test whether we can use Machine Learning algorithms for predicting disease outcome. To do so, I selected and extracted features from the raw data, including age, days between onset and outcome, gender, whether the patients were hospitalised, etc. Missing values were imputed and different model algorithms were used to predict outcome (death or recovery). The thus prepared dataset was devided into training and testing subsets. The test subset contained all cases with an unknown outcome. Before I applied the models to the test data, I further split the training data into validation subsets.

Data analysis echo system has grown all the way from SQL's to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run all these operations { Don't forget the cost factor here:) }. Things become challenging when we mature from aggregated/summarized analysis to Data mining, mathematical modeling, statistical modeling and predictive modeling.

Thus, given a sequence of frames corresponding to an utterance, the model is required to produce, for each frame, a probability distribution over the alphabet. During the training phase, the softmax outputs are fed into a CTC cost function (more on this shortly) which uses the actual transcripts to (i) score the model's predictions, and (ii) generate an error signal quantifying the accuracy of the model's predictions. The overall goal is to train the model to increase the overall score of its predictions relative to the actual transcripts. Training Empirically, we have found that using stochastic gradient descent with momentum paired with gradient clipping leads to the best performing models. Deeper networks (seven layers or more) also tend to perform better in general.

Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with a specific model and data set) are expected to be accurate. Confidence intervals are used to assess how reliable a statistical estimate is. Wide confidence intervals mean that your model is poor (and it is worth investigating other models), or that your data is very noisy if confidence intervals don't improve by changing the model (that is, testing a different theoretical statistical distribution for your observations.) Modern confidence intervals are model-free, data -driven: click here to see how to compute them. A more general framework to assess and reduce sources of variance is called analysis of variance.

Allows you to set up bounds on the regression parameters (similar to ridge regression). Does not use matrix inversion, thus numerically stable. Robust parameter estimation based on Monte-Carlo simulations and re-sampling. The source code can easily be modified to perform logistic regression. This package can be used by scientists, programmers, analysts or engineers with limited statistical knowledge.

Regression analysis marks the first step in predictive modeling. No doubt, it's fairly easy to implement. Neither it's syntax nor its parameters create any kind of confusion. But, merely running just one line of code, doesn't solve the purpose. Regression tells much more than that!

Bayesian inference is a popular method to build learning algorithms but it is hampered by the fact that its key object, the posterior probability distribution, is often uncomputable. Expectation Propagation (EP) (Minka [2001]) is a popular algorithm that solves this issue by computing a parametric approximation (e.g: Gaussian) to the density of the posterior. However, while it is known empirically to quickly compute fine approximations, EP is extremely poorly understood which prevents it from being adopted by a larger fraction of the community. The object of the present article is to shed intuitive light on EP, by relating it to other better understood methods. More precisely, we link it to using gradient descent to compute the Laplace approximation of a target probability distribution. We show that EP is exactly equivalent to performing gradient descent on a smoothed energy landscape: i.e: the original energy landscape convoluted with some smoothing kernel. This also relates EP to algorithms that compute the Gaussian approximation which minimizes the reverse KL divergence to the target distribution, a link that has been conjectured before but has not been proved rigorously yet. These results can help practitioners to get a better feel for how EP works, as well as lead to other new results on this important method. This article was submitted and accepted to the Advances in Approximate Bayesian Inference NIPS 2016 workshop (www.approximateinference.org).

Identifying measurable genetic indicators (or biomarkers) of a specific condition of a biological system is a key element of precision medicine. Indeed it allows to tailor diagnostic, prognostic and treatment choice to individual characteristics of a patient. In machine learning terms, biomarker discovery can be framed as a feature selection problem on whole-genome data sets. However, classical feature selection methods are usually underpowered to process these data sets, which contain orders of magnitude more features than samples. This can be addressed by making the assumption that genetic features that are linked on a biological network are more likely to work jointly towards explaining the phenotype of interest. We review here three families of methods for feature selection that integrate prior knowledge in the form of networks.