Gradient Boosted Decision Tree (GBDT) is a widely-used machine learning algorithm that has been shown to achieve state-of-the-art results on many standard data science problems. We are interested in its application to multioutput problems when the output is highly multidimensional. Although there are highly effective GBDT implementations, their scalability to such problems is still unsatisfactory. In this paper, we propose novel methods aiming to accelerate the training process of GBDT in the multioutput scenario. The idea behind these methods lies in the approximate computation of a scoring function used to find the best split of decision trees. These methods are implemented in SketchBoost, which itself is integrated into our easily customizable Python-based GPU implementation of GBDT called Py-Boost. Our numerical study demonstrates that SketchBoost speeds up the training process of GBDT by up to over 40 times while achieving comparable or even better performance.

Gradient Boosted Decision Trees (GBDTs) are dominant machine learning algorithms for modeling discrete or tabular data. Unlike neural networks with millions of trainable parameters, GBDTs optimize loss function in an additive manner and have a single trainable parameter per leaf, which makes it easy to apply high-order optimization of the loss function. In this paper, we introduce high-order optimization for GBDTs based on numerical optimization theory which allows us to construct trees based on high-order derivatives of a given loss function. In the experiments, we show that high-order optimization has faster per-iteration convergence that leads to reduced running time. Our solution can be easily parallelized and run on GPUs with little overhead on the code. Finally, we discuss future potential improvements such as automatic differentiation of arbitrary loss function and combination of GBDTs with neural networks.

Random forests (RFs) [Bre01] are machine learning models with various applications in areas like E-commerce, Finance and Medicine. They consist of multiple decision trees that use different subsets of the available features. Given an input, every tree makes an individual decision and the output of the random forest is obtained by a majority vote. They have low risk of overfitting; support both classification and regression tasks and come equipped with some feature importance measures [Bre01]. However, feature importance measures can be too simplistic as they can represent neither joint effects of features (e.g., multi-drug interactions) nor non-monotonicity (e.g., increasing the weight may be healthy for an underweight person, but not for an overweight person). In recent years, a variety of other explanation methods has been proposed. Modelagnostic feature importance measures like LIME [RSG16], SHAP [LL17] and MAPLE [PMT18] have similar limitations like the feature importance measures defined for random forests.

This article will cover the health care concern of diabetes that is driving the lifestyle of many people worldwide. This article will cover the usage of machine learning models to create a predictive system. This model will use random-forest to predict if patients have diabetes or not. The article will outline the requirements needed to set up our database GridDB. Following that, we will briefly describe our dataset and model.

This is the first in a series of posts that illustrate what our data team is up to, experimenting with, and building'under the hood' at CitizenNet. He has been involved in web-scale machine learning and information retrieval for over 10 years. One of the first posts we published spoke at a high level of the technical problem CitizenNet is trying to solve. In essence, we are trying to predict what combinations of demographic and interest targets will be interested in some piece of content. On the CitizenNet platform, a user would create a project that would define (broadly) the target audience, the pieces of Facebook content they are looking to promote, and other campaign and financial information. Behind the scenes, a robust prediction system builds the targets for the project.

This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in critical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based explanations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation algorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to original model's labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject experiments (for interpretability) We find that Tree-ICE outperforms the baseline in interpretability and generates more human readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.

Taking SpongeBob SquarePants' mood as an example, based on historical data, there are two factors that affect whether he is happy or upset. So, if the whole mood table of Mr. SquarePants is visualized into a scatter plot, it will look as the following: Decision trees are used in classification problems to find a line(s) that separates the data as perfectly as possible. The separation process is done by measuring the homogeneity or similarity of the data. Attempting to separate data, a vertical line is drawn to separate happy SpongeBob from upset SpongeBob, taking into consideration one feature only -which is the number of jellyfish that were hunted. A vertical line at the number 10 on the x-axis can work as a separator, so if the number of jellyfish hunted is less than 10 SpongeBob is upset otherwise, he is happy.

Machine learning models are incorporated in different fields and disciplines in which some of them require a high level of accountability and transparency, for example, the healthcare sector. With the General Data Protection Regulation (GDPR), the importance for plausibility and verifiability of the predictions made by machine learning models has become essential. A widely used category of explanation techniques attempts to explain models’ predictions by quantifying the importance score of each input feature. However, summarizing such scores to provide human-interpretable explanations is challenging. Another category of explanation techniques focuses on learning a domain representation in terms of high-level human-understandable concepts and then utilizing them to explain predictions. These explanations are hampered by how concepts are constructed, which is not intrinsically interpretable. To this end, we propose Concept-based Local Explanations with Feedback (CLEF), a novel local model agnostic explanation framework for learning a set of high-level transparent concept definitions in high-dimensional tabular data that uses clinician-labeled concepts rather than raw features. CLEF maps the raw input features to high-level intuitive concepts and then decompose the evidence of prediction of the instance being explained into concepts. In addition, the proposed framework generates counterfactual explanations, suggesting the minimum changes in the instance’s concept based explanation that will lead to a different prediction. We demonstrate with simulated user feedback on predicting the risk of mortality. Such direct feedback is more effective than other techniques, that rely on hand-labelled or automatically extracted concepts, in learning concepts that align with ground truth concept definitions.

Data splitting is a simple sub-step in machine learning modelling or data modelling, using which we can have a realistic understanding of model performance. Also, it helps the model to generalize well to unknown or unseen data. Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions. DSW's flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.

We demonstrate and visualize different explanation methods, how to interpret them, and provide a complete Python package (scikit-explain) to allow future researchers to explore these products. We also highlight the frequent disagreement between explanation methods for feature rankings and feature effects and provide practical advice for dealing with these disagreements. We used ML models developed for severe weather prediction and sub-freezing road surface temperature prediction to generalize the behavior of the different explanation methods. For feature rankings, there is substantially more agreement on the set of top features (e.g., on average, two methods agree on 6 of the top 10 features) than on specific rankings (on average, two methods only agree on the ranks of 2-3 features in the set of top 10 features). On the other hand, two feature effect curves from different methods are in high agreement as long as the phase space is well sampled. Finally, a lesser-known method, tree interpreter, was found comparable to SHAP for feature effects, and with the widespread use of random forests in geosciences and computational ease of tree interpreter, we recommend it be explored in future research.