There are three main reasons We present LCE, a Local Cascade Ensemble for that justify the use of ensembles over single classifiers [Dietterich, traditional (tabular) multivariate data classification, 2000]: statistical (reduce the risk of choosing the and its extension LCEM for Multivariate Time Series wrong classifier by averaging when the amount of training (MTS) classification. LCE is a new hybrid ensemble data available is too small compared to the size of the hypothesis method that combines an explicit boostingbagging space), computational (local search from many different approach to handle the bias-variance tradeoff starting points may provide a better approximation to faced by machine learning models and an implicit the true unknown function than any of the individual classifier), divide-and-conquer approach to individualize and representational (expansion of the space of representable classifier errors on different parts of the training functions).

Noname manuscript No. (will be inserted by the editor) Abstract Bayesian Additive Regression Trees (BART) 1 Introduction is a tree-based machine learning method that has been successfully applied to regression and classification problems. Bayesian Additive Regression Trees (BART) is a statistical BART assumes regularisation priors on a set of method proposed by Chipman et al (2010) that has trees that work as weak learners and is very flexible for become popular in recent years due to its competitive predicting in the presence of non-linearity and highorder performance on regression and classification problems, interactions. In this paper, we introduce an extension when compared to other supervised machine learning of BART, called Model Trees BART (MOTR-methods, such as Random Forests (RF) (Breiman, 2001) BART), that considers piecewise linear functions at node and Gradient Boosting (GB) (Friedman, 2001). In MOTR-BART, differs from other tree-based methods as it controls the rather than having a unique value at node level for the structure of each tree via a prior distribution and generates prediction, a linear predictor is estimated considering the predictions via an MCMC backfitting algorithm the covariates that have been used as the split variables that is responsible for accepting and rejecting the in the corresponding tree. In our approach, local linearities proposed trees along the iterations.

This article was published as a part of the Data Science Blogathon. Decision Tree is one of the most intuitive and effective tools present in a Data Scientist's toolkit. It has an inverted tree-like structure that was once used only in Decision Analysis but is now a brilliant Machine Learning Algorithm as well, especially when we have a Classification problem on our hands. These decision trees are well-known for their capability to capture the patterns in the data. But, excess of anything is harmful, right?

A "non-greedy" variation of the random forest algorithm is presented to better uncover feature interdependencies inherent in complex systems. Conventionally, random forests are built from "greedy" decision trees which each consider only one split at a time during their construction. In contrast, the decision trees included in this random forest algorithm each consider three split nodes simultaneously in tiers of depth two. It is demonstrated on synthetic data and bitcoin price time series that the non-greedy version significantly outperforms the greedy one if certain non-linear relationships between feature-pairs are present. In particular, both greedy and a non-greedy random forests are trained to predict the signs of daily bitcoin returns and backtest a long-short trading strategy. The better performance of the non-greedy algorithm is explained by the presence of "XOR-like" relationships between long-term and short-term technical indicators. When no such relationships exist, performance is similar. Given its enhanced ability to understand the feature-interdependencies present in complex systems, this non-greedy extension should become a standard method in the toolkit of data scientists.

Random Forest or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees. Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the "bagging" method. The general idea of the bagging method is that a combination of learning models increases the overall result. Random Forest is an ensemble method.

A decision tree is a non-parametric supervised machine learning algorithm. It is extremely useful in classifying or labels the object. It works for both categorical and continuous datasets. It is like a tree structure in which the root node and its child node should be present. It has a child node that denotes a feature of the dataset. Prediction can be made with a leaf or terminal node.

Many computationally expensive tasks for machine learning can be made parallel by splitting the work across multiple CPU cores, referred to as multi-core processing. Common machine learning tasks that can be made parallel include training models like ensembles of decision trees, evaluating models using resampling procedures like k-fold cross-validation, and tuning model hyperparameters, such as grid and random search. Using multiple cores for common machine learning tasks can dramatically decrease the execution time as a factor of the number of cores available on your system. A common laptop and desktop computer may have 2, 4, or 8 cores. Larger server systems may have 32, 64, or more cores available, allowing machine learning tasks that take hours to be completed in minutes. In this tutorial, you will discover how to configure scikit-learn for multi-core machine learning.

Modern machine learning models (such as deep neural networks and boosting decision tree models) have become increasingly popular in financial market prediction, due to their superior capacity to extract complex non-linear patterns. However, since financial datasets have very low signal-to-noise ratio and are non-stationary, complex models are often very prone to overfitting and suffer from instability issues. Moreover, as various machine learning and data mining tools become more widely used in quantitative trading, many trading firms have been producing an increasing number of features (aka factors). Therefore, how to automatically select effective features becomes an imminent problem. To address these issues, we propose DoubleEnsemble, an ensemble framework leveraging learning trajectory based sample reweighting and shuffling based feature selection. Specifically, we identify the key samples based on the training dynamics on each sample and elicit key features based on the ablation impact of each feature via shuffling. Our model is applicable to a wide range of base models, capable of extracting complex patterns, while mitigating the overfitting and instability issues for financial market prediction. We conduct extensive experiments, including price prediction for cryptocurrencies and stock trading, using both DNN and gradient boosting decision tree as base models. Our experiment results demonstrate that DoubleEnsemble achieves a superior performance compared with several baseline methods.

Differentiable forest is an ensemble of decision trees with full differentiability. Its simple tree structure is easy to use and explain. With full differentiability, it would be trained in the end-to-end learning framework with gradient-based optimization method. In this paper, we propose tree attention block(TAB) in the framework of differentiable forest. TAB block has two operations, squeeze and regulate. The squeeze operation would extract the characteristic of each tree. The regulate operation would learn nonlinear relations between these trees. So TAB block would learn the importance of each tree and adjust its weight to improve accuracy. Our experiment on large tabular dataset shows attention augmented differentiable forest would get comparable accuracy with gradient boosted decision trees(GBDT), which is the state-of-the-art algorithm for tabular datasets. And on some datasets, our model has higher accuracy than best GBDT libs (LightGBM, Catboost, and XGBoost). Differentiable forest model supports batch training and batch size is much smaller than the size of training set. So on larger data sets, its memory usage is much lower than GBDT model. The source codes are available at https://github.com/closest-git/QuantumForest.

We study the problem of balancing effectiveness and efficiency in automated feature selection. After exploring many feature selection methods, we observe a computational dilemma: 1) traditional feature selection is mostly efficient, but difficult to identify the best subset; 2) the emerging reinforced feature selection automatically navigates to the best subset, but is usually inefficient. Can we bridge the gap between effectiveness and efficiency under automation? Motivated by this dilemma, we aim to develop a novel feature space navigation method. In our preliminary work, we leveraged interactive reinforcement learning to accelerate feature selection by external trainer-agent interaction. In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF). Specifically, IRL is to create an interactive feature selection loop and DTF is to feed structured feature knowledge back to the loop. First, the tree-structured feature hierarchy from decision tree is leveraged to improve state representation. In particular, we represent the selected feature subset as an undirected graph of feature-feature correlations and a directed tree of decision features. We propose a new embedding method capable of empowering graph convolutional network to jointly learn state representation from both the graph and the tree. Second, the tree-structured feature hierarchy is exploited to develop a new reward scheme. In particular, we personalize reward assignment of agents based on decision tree feature importance. In addition, observing agents' actions can be feedback, we devise another reward scheme, to weigh and assign reward based on the feature selected frequency ratio in historical action records. Finally, we present extensive experiments on real-world datasets to show the improved performance.