As the name of the suggests, cross-validation is the next fun thing after learning Linear Regression because it helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Everything is explained below with Code. We are copying the target in dataset to y variable. To see the dataset uncomment the print line.

As pointed in this Kdnuggets article, it's often the case that we only have a few examples of the thing that we want to predict in our data. The use cases are countless: only a small part of our website visitors purchase eventually, only a few of our transactions are fraudulent, etc. This is a real problem when using Machine Learning. That's because the algorithms usually need many examples of each class to extract the general rules in your data, and the instances in minority classes can be discarded as noise, causing some useful rules to never be found. The Kdnuggets article explained several techniques that can be used to address this problem.

This is great, in the sense that you don't have to worry about accidently forgetting to carry the 1 or remember how each rule in calculus operates. It is still great to have a general understanding of some of the equations you can utilize, distributions you can model and general statistics rules that can help clean up your data! We need to quickly lay out some definitions. In this post we will talk about discrete variables. If you have not heard the term before this references variables that are of a limited set. It actually could include numbers that are decimals pending on the set of variables you are using. However, these rules need to be established. For instance, you can't have 3.5783123 medical procedures in real life.

This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.

In high-dimensional linear models, the sparsity assumption is typically made, stating that most of the parameters are equal to zero. Under the sparsity assumption, estimation and, recently, inference have been well studied. However, in practice, sparsity assumption is not checkable and more importantly is often violated, with a large number of covariates expected to be associated with the response, indicating that possibly all, rather than just a few, parameters are non-zero. A natural example is a genome-wide gene expression profiling, where all genes are believed to affect a common disease marker. We show that existing inferential methods are sensitive to the sparsity assumption, and may, in turn, result in the severe lack of control of Type-I error. In this article, we propose a new inferential method, named CorrT, which is robust to model misspecification and adaptive to the sparsity assumption. CorrT is shown to have Type I error approaching the nominal level for \textit{any} models and Type II error approaching zero for sparse and many dense models. In fact, CorrT is also shown to be optimal in a variety of frameworks: sparse, non-sparse and hybrid models where sparse and dense signals are mixed. Numerical experiments show a favorable performance of the CorrT test compared to the state-of-the-art methods.

Science, technology, engineering, and math (STEM) fields play growing roles in national and international economies by driving innovation and generating high salary jobs. Yet, the US is lagging behind other highly industrialized nations in terms of STEM education and training. Furthermore, many economic forecasts predict a rising shortage of domestic STEM-trained professions in the US for years to come. One potential solution to this deficit is to decrease the rates at which students leave STEM-related fields in higher education, as currently over half of all students intending to graduate with a STEM degree eventually attrite. However, little quantitative research at scale has looked at causes of STEM attrition, let alone the use of machine learning to examine how well this phenomenon can be predicted. In this paper, we detail our efforts to model and predict dropout from STEM fields using one of the largest known datasets used for research on students at a traditional campus setting. Our results suggest that attrition from STEM fields can be accurately predicted with data that is routinely collected at universities using only information on students' first academic year. We also propose a method to model student STEM intentions for each academic term to better understand the timing of STEM attrition events. We believe these results show great promise in using machine learning to improve STEM retention in traditional and non-traditional campus settings.

Did you pay for an expensive pay-per-view or streaming pass to watch the hyped-up boxing match between Floyd Mayweather and Conor McGregor, only to boil with rage as your access went down? Numerous reports have revealed that servers across the US crashed or buckled under demand for the fight, creating outages serious enough that organizers delayed the fight to make sure people could tune in. Mayweather himself said that pay-per-view servers in California and Florida crashed, while Showtime and UFC failed to load, ran into login trouble and otherwise couldn't keep up with interest. The pay-per-view issues at a minimum are known to have affected TV providers like Comcast, Atlantic Broadband and Frontier, although it's not clear how large the scope of the failures was at this stage. Problems like this aren't completely unprecedented -- Mayweather's fight against Manny Pacquiao created hiccups of its own.

Microsoft's speech recognition efforts have hit a significant milestone. It can now transcribe human speech with a 5.1% error rate, Microsoft technical fellow Xuedong Huang wrote in a blog post -- the same error rate as humans. Microsoft actually thought it hit this point last year, when it reached 5.9%, the word error rate it had measured for humans. But then other researchers carried out separate studies and pegged the human error level as slightly lower, 5.1%. But it has now achieved this -- reducing its error rate by 12%, and using AI techniques like "neural-net based acoustic and language models."

The biggest fight of 2017 and possibly the No.1 pay-per-view of all time is finally almost here. Floyd Mayweather and Conor McGregor will go head-to-head Saturday night at T-Mobile Arena in Las Vegas, pitting the greatest boxer of his generation against the biggest star in UFC history. It all starts at 9 p.m. EDT on Showtime PPV with three undercard bouts preceding the main event. Watching the fight on TV will cost fans $99.99, making for a potential record-setting night. Preliminary bouts are scheduled to start on FOX at 7 p.m. EDT, and those fights can be seen with a free live stream online with FOX Sports GO.

The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space. Both the neural parameters and Bayesian priors are learned jointly using tailored variational inference. The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances. We apply our model in video representation learning. Our method is able to discover highly interpretable activity hierarchies, and obtain improved clustering accuracy and generalization capacity based on the learned rich representations.