Tree boosting has empirically proven to be efficient for predictive mining for both classification and regression. For many years, MART (multiple additive regression trees) has been the tree boosting method of choice. But a starting from 2015, a first to try, always winning algorithm surged to the surface: XGBoost. This algorithm re-implements the tree boosting and gained popularity by winning Kaggle and other data science competition. The paper introduce in first place the supervised learning task and discuss the model selection techniques.

For machine learning, you can also use these libraries to build learning models. However, doing so requires that you have a strong appreciation of the mathematical foundation for the various machine learning algorithms.

Myths aside, artificial intelligence is as prone to bias as the human kind. The good news is that the biases in algorithms can also be diagnosed and treated. Companies are moving quickly to apply machine learning to business decision making. New programs are constantly being launched, setting complex algorithms to work on large, frequently refreshed data sets. The speed at which this is taking place attests to the attractiveness of the technology, but the lack of experience creates real risks. Algorithmic bias is one of the biggest risks because it compromises the very purpose of machine learning. This often-overlooked defect can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions.

Over time, TensorFlow has grown in popularity and is now being used by developers for solving problems using deep learning methods for image recognition, video detection, text processing like sentiment analysis, etc. Like any other library, you may need some time to get used to the concepts that TensorFlow is built on. And, once you do, with the help of documentation and community support, representing problems as data graphs and solving them with TensorFlow can make machine learning at scale a less tedious process. In TensorFlow, constants are created using the constant function which takes a few parameters: Value, dtype (data type), shape, name and (verify_shape) shape verification.

Anyone who has thought about scaling a business or building a network is familiar with a dynamic referred to as the "network effect." The more buyers and sellers who use a marketplace like eBay, for example, the more useful it becomes. Well, the data network effect is a dynamic in which increased use of a service actually improves the service, such as how machine-learning models generally grow more accurate as a result of training from larger and larger volumes of data. Autonomous vehicles and other smart robots rely on sensors that generate increasingly massive volumes of highly varied data. This data is used to build better AI models that robots rely on to make real-time decisions and navigate real-world environments.

Suppose I have a classification neuralnet for which I compute the confusion matrix on the validation set after my network has converged. What ways are there of using this matrix to reliably increase the accuracy on unseen data? I know of setting a per-class minimum confidence threshold. But would it make sense to reponder the softmax predictions knowing that some class A is often misclassified as B by the network etc...?

In this paper we use information-theoretic measures to provide a theory and tools to analyze the flow of information from a discrete, multivariate source of information $\overline X$ to a discrete, multivariate sink of information $\overline Y$ joined by a distribution $P_{\overline X \overline Y}$. The first contribution is a decomposition of the maximal potential entropy of $(\overline X, \overline Y)$ that we call a balance equation, that can also be split into decompositions for the entropies of $\overline X$ and $\overline Y$ respectively. Such balance equations accept normalizations that allow them to be represented in de Finetti entropy diagrams, our second contribution. The most important of these, the aggregate Channel Multivariate Entropy Triangle CMET is an exploratory tool to assess the efficiency of multivariate channels. We also present a practical contribution in the application of these balance equations and diagrams to the assessment of information transfer efficiency for PCA and ICA as feature transformation and selection procedures in machine learning applications.

Missing data is an expected issue when large amounts of data is collected, and several imputation techniques have been proposed to tackle this problem. Beneath classical approaches such as MICE, the application of Machine Learning techniques is tempting. Here, the recently proposed missForest imputation method has shown high imputation accuracy under the Missing (Completely) at Random scheme with various missing rates. In its core, it is based on a random forest for classification and regression, respectively. In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5.0 algorithm or modified random forest procedures. In particular, other resampling strategies within the random forest protocol are suggested. In an extensive simulation study, we analyze their performances for continuous, categorical as well as mixed-type data. Therein, MissBooPF, a combination of the stochastic gradient tree boosting method together with the parametrically bootstrapped random forest method, appeared to be promising. Finally, an empirical analysis focusing on credit information and Facebook data is conducted.

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.

Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL). Hierarchical RL (HRL) tackles this problem by learning a hierarchical policy, where multiple option policies are in charge of different strategies corresponding to modes of a reward function and a gating policy selects the best option for a given context. Although HRL has been demonstrated to be promising, current state-of-the-art methods cannot still perform well in complex real-world problems due to the difficulty of identifying modes of the reward function. In this paper, we propose a novel method called hierarchical policy search via return-weighted density estimation (HPSDE), which can efficiently identify the modes through density estimation with return-weighted importance sampling. Our proposed method finds option policies corresponding to the modes of the return function and automatically determines the number and the location of option policies, which significantly reduces the burden of hyper-parameters tuning. Through experiments, we demonstrate that the proposed HPSDE successfully learns option policies corresponding to modes of the return function and that it can be successfully applied to a challenging motion planning problem of a redundant robotic manipulator.