XGBoost is an implementation of gradient boosted decision trees designed for speed and performance that is dominative competitive machine learning. In this post you will discover how you can install and create your first XGBoost model in Python. How to Develop Your First XGBoost Model in Python with scikit-learn Photo by Justin Henry, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. Assuming you have a working SciPy environment, XGBoost can be installed easily using pip.

This project looks into the application of Machine Learning (ML) techniques in the prediction of Immunogenicity (Categorical; Positive or Negative) based on a peptide and its associated amino acid properties. This study uses peptide data from the Immune Epitode Database (IEDB). The R package "Peptides" has been used to compute the amino acid properties and mashup with peptide data to enable the use of ML algorithms for immunogenicity analysis, particularly, the algorithms that are more efficient with numeric and categorical data instead of string sequence. Tensorflow is an open source software library ML that provides linear regression and classification algorithms (open sourced by Google in Nov 2015) for multi-dimensional arrays (aka "Tensors"). K-fold cross-validation as well as hold-out of test data was used to train and test the generated models.

Machine Learning is all about using computer systems and applying statistical techniques and algorithms to identify patterns in data, learn from it and provide data-driven trends, predictions and decisions. Machine learning algorithms have two flavors: Supervised learning and Unsupervised learning.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner.

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important aspects of such data that lead to problems with current methods include the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' connectivity behaviors. Motivated by this application, we develop a novel method for Locally Adaptive DYnamic (LADY) network inference. The proposed model relies on a dynamic latent space representation in which each actor's position evolves in time via stochastic differential equations. Using a state space representation for these stochastic processes and P\'olya-gamma data augmentation, we develop an efficient MCMC algorithm for posterior inference along with tractable procedures for online updating and forecasting of future networks. We evaluate performance in simulation studies, and consider an application to face-to-face contacts among individuals in a primary school.

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular "graph convolutions", a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph---atoms, bonds, distances, etc.---which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

Linear regression, the fitting of linear relationships among variables in a data set, is a standard tool in data analysis. In particular, for the sake of interpretability and utility in further analysis, we desire to find highly sparse linear relationships, i.e., involving only a few variables. Of course, such simple linear relationships often will not hold across an entire population. But, more frequently there will exist conditions - perhaps a range of parameters or a segment of a larger population - under which such sparse models fit the data quite well. For example, Rosenfeld et al. [16] used data mining heuristics to identify small segments of a population in which a few additional risk factors were highly predictive of certain kinds of cancer, whereas these same risk factors were not significant in the overall population. Simple rules for special cases may also hint at the more complex general rules. More generally, we need to develop new techniques to reason about populations in which most members are atypical in some way, which are colloquially (and somewhat abusively) referred to as long-tailed distributions. We are seeking principled alternatives to ad-hoc approaches such as trying a variety of methods for clustering the data and hoping that the identified clusters can be modeled well.

Local network community detection is the task of finding a single community of nodes concentrated around few given seed nodes in a localized way. Conductance is a popular objective function used in many algorithms for local community detection. This paper studies a continuous relaxation of conductance. We show that continuous optimization of this objective still leads to discrete communities. We investigate the relation of conductance with weighted kernel k-means for a single community, which leads to the introduction of a new objective function, $\sigma$-conductance. Conductance is obtained by setting $\sigma$ to $0$. Two algorithms, EMc and PGDc, are proposed to locally optimize $\sigma$-conductance and automatically tune the parameter $\sigma$. They are based on expectation maximization and projected gradient descent, respectively. We prove locality and give performance guarantees for EMc and PGDc for a class of dense and well separated communities centered around the seeds. Experiments are conducted on networks with ground-truth communities, comparing to state-of-the-art graph diffusion algorithms for conductance optimization. On large graphs, results indicate that EMc and PGDc stay localized and produce communities most similar to the ground, while graph diffusion algorithms generate large communities of lower quality.