Learning to Classify with Branching Tests: "A decision tree takes as input an object or situation described by a set of properties, and outputs a yes/no decision. Decision trees therefore represent Boolean functions. Functions with a larger range of outputs can also be represented...."
– Artificial Intelligence: A Modern Approach. By Stuart Russell & Peter Norvig. 2002. Section 18.3; page 531.
One way could be to identify some of the most critical parameters to look for in any AI solution, and to rate/label them on a standard scale. Few such parameters are discussed below. Perhaps the community and policymakers can crystallize these further, and add to the list. Decision trees, Random forest, Gradient boosting, Monte Carlo, to name a few. The use of any one of these (say, Regression) in a solution can technically qualify it as AI-enabled, but it would not be very accurate or useful for a user.
In its update to its National Artificial Intelligence Research And Development Strategic Plan, the White House's Office of Science and Technology Policy has set new objectives for federal AI research. WHY IT MATTERS The strategic plan boils down to eight strategies for how government can better enable development of safe and effective AI and machine learning technologies for healthcare and other industries. The 50-page document takes special interest in ensuring that data used to power AI is trustworthy and that the algorithms used to process it are understandable – not least in healthcare. "A key research challenge is increasing the'explainability' or ''transparency' of AI," according to the report. "Many algorithms, including those based on deep learning, are opaque to users, with few existing mechanisms for explaining their results. This is especially problematic for domains such as healthcare, where doctors need explanations to justify a particular diagnosis or a course of treatment. AI techniques such as decision-tree induction provide built-in explanations but are generally less accurate. Thus, researchers must develop systems that are transparent, and intrinsically capable of explaining the reasons for their results to users."
We can further evaluate the variable interactions by plotting the probability of a prediction against the variables making up the interaction. However, there is an error when the input supplied is a model created with parsnip. There is no error when the model is created directly from the randomForest package. In this case, we can place it side by side with the ggplot of the distribution of heart disease in the test set.
Customer churn, also known as customer attrition, occurs when customers stop doing business with a company. The companies are interested in identifying segments of these customers because the price for acquiring a new customer is usually higher than retaining the old one. For example, if Netflix knew a segment of customers who were at risk of churning they could proactively engage them with special offers instead of simply losing them. In this post, we will create a simple customer churn prediction model using Telco Customer Churn dataset. We chose a decision tree to model churned customers, pandas for data crunching and matplotlib for visualizations.
The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for "wide" datasets, current implementations such as Google's PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Google's PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.
This tutorial walks you through a comparison of XGBoost and Random Forest, two popular decision tree algorithms, and helps you identify the best use cases for ensemble techniques like bagging and boosting. By following the tutorial, you'll learn: Understanding the benefits of bagging and boosting--and knowing when to use which technique--will lead to less variance, lower bias, and more stability in your machine learning models.
The SMART Forecasting team at Walmart Labs is tasked with providing demand forecasts for over 70 million store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste. Our algorithm strategy was to build a suite of machine learning models and deploy them at scale to generate bespoke solutions for (oh so many!) store-item-week combinations. Random Forests would be part of this suite. We went through the traditional model development workflow of data discovery, identifying demand drivers, feature engineering, training, cross validation and testing.
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism. This lack of interpretability is a strong limitation for applications involving critical decisions, typically the analysis of production processes in the manufacturing industry. In such critical contexts, models have to be interpretable, i.e., simple, stable, and predictive. To address this issue, we design SIRUS (Stable and In-terpretable RUle Set), a new classification algorithm based on random forests, which takes the form of a short list of rules. While simple models are usually unstable with respect to data perturbation, SIRUS achieves a remarkable stability improvement over cutting-edge methods. Furthermore, SIRUS inherits a predictive accuracy close to random forests, combined with the simplicity of decision trees. These properties are assessed both from a theoretical and empirical point of view, through extensive numerical experiments based on our R/C++ software implementation sirus.
I posted this a few months ago and had some great feedback. I've put some work into the model and have just released the latest update. It uses a modified version of C4.5 decision trees and a load of other adjustments. Think it is working better now after some changes around the classification process.
Under the usual IV assumptions, our method discovers and tests heterogeneity in H-CATEs by using matching, CART, and closed testing, all without the need to do sample splitting. The latter is achieved by taking the absolute value of the adjusted pairwise differences to conceal the instrument assignment. Our method was shown to strongly control the familywise error rate. We conducted a simulation study to examine the power of our method under varying degrees of 18 compliance and effect heterogeneity and showed that our method can detect wide variety of heterogeneity. Our method was used to study the effect of Medicaid on the number of days an individual's physical or mental health did not prevent their usual activities where we used the lottery selection as an instrument. It was found that Medicaid has a larger impact on improving the number of days not impeded upon by their health for complying, older, non-Asian men who selected English materials at lottery sign-up and for complying, younger, less educated individuals who selected English materials at lottery sign-up.