Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore,it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.

There are several types of predictive models. These models usually have several input columns and one target or outcome column, which is the variable to be predicted. So basically, a model performs mapping between inputs and an output, finding-mysteriously, sometimes-the relationships between the input variables in order to predict any other variable. As you may notice, it has some commonalities with a human being who reads the environment processes the information and performs a certain action. It's about becoming familiar with one of the most-used predictive models: Random Forest (official algorithm site), implemented in R, one of the most-used models due to its simplicity in tuning and robustness across many different types of data.

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.

In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is based on harmonic analysis and approximation theoretical elements, and as we show through extensive experimentation, our wavelet based method generally outperforms existing methods, particularly in difficult scenarios of class unbalance and mislabeling in the training data.

Random forests have become an important tool for improving accuracy in regression problems since their popularization by (Breiman, 2001) and others. In this paper, we revisit a random forest model originally proposed by (Breiman, 2004) and later studied by (Biau, 2012), where a feature is selected at random and the split occurs at the midpoint of the block containing the chosen feature. If the regression function is sparse and depends only on a small, unknown subset of $ S $ out of $ d $ features, we show that given $ n $ observations, this random forest model outputs a predictor that has a mean-squared prediction error of order $ \left(n\sqrt{\log^{S-1} n}\right)^{-\frac{1}{S\log2+1}} $. When $ S \leq \lfloor 0.72 d \rfloor $, this rate is better than the minimax optimal rate $ n^{-\frac{2}{d+2}} $ for $ d $-dimensional, Lipschitz function classes. As a consequence of our analysis, we show that the variance of the forest decays with the depth of the tree at a rate that is independent of the ambient dimension, even when the trees are fully grown. In particular, if $ \ell_{avg} $ (resp. $ \ell_{max} $) is the average (resp. maximum) number of observations per leaf node, we show that the variance of this forest is $ \Theta\left(\ell^{-1}_{avg}(\sqrt{\log n})^{-(S-1)}\right) $, which for the case of $ S = d $, is similar in form to the lower bound $ \Omega\left(\ell^{-1}_{max}(\log n)^{-(d-1)}\right) $ of (Lin and Jeon, 2006) for any random forest model with a nonadaptive splitting scheme. We also show that the bias is tight for any linear model with nonzero parameter vector. Thus, we completely characterize the fundamental limits of this random forest model. Our new analysis also implies that better theoretical performance can be achieved if the trees are grown less aggressively (i.e., grown to a shallower depth) than previous work would otherwise recommend.

Joanne Lin, a student at Thinkful's data science bootcamp, decided to jump in and find insights that can help shelters get more pets rescued.

Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the "smart grid" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators. While the statistics and machine learning literature offers many proposed methods for electricity price prediction, there is no consensus supporting a single best approach. We test two contrasting machine learning approaches for predicting electricity prices, regression decision trees and recurrent neural networks (RNNs), and compare them to a more traditional ARIMA implementation. We conduct the analysis on a challenging dataset of electricity prices from ERCOT, in Texas, where price fluctuation is especially high. We find that regression decision trees in particular achieves high performance compared to the other methods, suggesting that regression trees should be more carefully considered for electricity price forecasting.

Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest models optimized for nonparametric conditional density estimation, including joint densities for multiple responses. This enables analysis of conditional probability distributions which is useful for propagating uncertainty and of joint distributions that describe relationships between multiple responses and covariates. RFCDE is released under the MIT open-source license and can be accessed at https://github.com/tpospisi/rfcde . Both R and Python versions, which call a common C++ library, are available.

In recent work on classification in crowdsourcing systems, complex questions are often replaced by a set of simpler binary questions (microtasks) to enhance classification performance [1]-[4]. This is especially helpful in situations where crowd workers lack expertise for responding to complex questions directly. Each worker is given the entire set of questions in a batch mode and the workers provide their responses in the form of a vector. These binary questions can be posted as "microtasks" on crowdsourcing platforms like Amazon Mechanical Turk [5]. To improve classification performance in crowdsourcing systems, most of the works in the literature focus on enhancing the quality of individual tests, by designing fusion rules to combine decisions from heterogeneous workers [1]-[4], [6], [7], and by investigating the assignment of different tests to different workers depending upon their skill level [8], [9].

In this post, I am going to compare two popular ensemble methods, Random Forests (RM) and Gradient Boosting Machine (GBM). GBM and RF both are ensemble learning methods and predict (regression or classification) by combining the outputs from individual trees (we assume tree-based GBM or GBT). They have all the strengths and weaknesses of the ensemble methods mentioned in my previous post. So, here we compare them only with respect to each other. GBM and RF differ in the way the trees are built: the order and the way the results are combined.