Both in the domains of Feature Selection and Interpretable AI, there exists a desire to `rank' features based on their importance. Such feature importance rankings can then be used to either: (1) reduce the dataset size or (2) interpret the Machine Learning model. In the literature, however, such Feature Rankers are not evaluated in a systematic, consistent way. Many papers have a different way of arguing which feature importance ranker works best. This paper fills this gap, by proposing a new evaluation methodology. By making use of synthetic datasets, feature importance scores can be known beforehand, allowing more systematic evaluation. To facilitate large-scale experimentation using the new methodology, a benchmarking framework was built in Python, called fseval. The framework allows running experiments in parallel and distributed over machines on HPC systems. By integrating with an online platform called Weights and Biases, charts can be interactively explored on a live dashboard. The software was released as open-source software, and is published as a package on the PyPi platform. The research concludes by exploring one such large-scale experiment, to find the strengths and weaknesses of the participating algorithms, on many fronts.

This paper presents CoLLIE: a simple, yet effective model for continual learning of how language is grounded in vision. Given a pre-trained multimodal embedding model, where language and images are projected in the same semantic space (in this case CLIP by OpenAI), CoLLIE learns a transformation function that adjusts the language embeddings when needed to accommodate new language use. This is done by predicting the difference vector that needs to be applied, as well as a scaling factor for this vector, so that the adjustment is only applied when needed. Unlike traditional few-shot learning, the model does not just learn new classes and labels, but can also generalize to similar language use and leverage semantic compositionality. We verify the model's performance on two different tasks of identifying the targets of referring expressions, where it has to learn new language use. The results show that the model can efficiently learn and generalize from only a few examples, with little interference with the model's original zero-shot performance.

On this part we shall consider a curious example, I must say. As said our reference for this case study [7, p. 74]: "If we were the only ones in the world with access to this info, we could be the best Boston real-estate investors in 1978! Unless, somehow, someone were able to build an even more accurate estimate . . This is the Boston House problem. Essentially, the problem is used as benchmark for machine learning, generally, on competitions. "to estimate the median value of the house prices in a neighborhood (MEDV) given all the input features from the neighborhood." This problem is different from the previous one only because we have several inputs instead of just one. This problem is closer from reality since most problem, at least the one that can be useful, will have to do more than humans can do either with simple models or by head; and machine learning is good at it! As long as you have the computer power, and time to wait, they solve it with their feet on their backs, if they have any! One interesting reflection we shall do is regarding interpreting their inner workings, beyond just prediction. Prediction is the process by which we want to know what is next in time, on a system (e.g., stock market or demands on a company). "Is there any way to peek inside the model to see how it understands the data?….

I am writing this article to make a deep understanding of the similarity and differences between Linear and Logistic regression algorithm and their working with help of their code. As we know that Linear Regression is a supervised Machine Learning algorithm, is a statistical method which is used to study of relationships between two continuous variables i.e. dependent and independent variable. It also predicts continuous values and finds the best fitting line that describes variables. Logistic Regression is used to predict categorical data. It is an another supervised machine learning algorithm used statistically analyzing a dataset in which there are one or more independent variables that determine an outcome.

In this article, I will take you through how to train a model for the task of heart disease prediction using Machine Learning. I will use the Logistic Regression algorithm in machine learning to train a model to predict heart disease. Predicting and diagnosing heart disease is the biggest challenge in the medical industry and relies on factors such as the physical examination, symptoms and signs of the patient. Factors that influence heart disease are body cholesterol levels, smoking habit and obesity, family history of illnesses, blood pressure, and work environment. Machine learning algorithms play an essential and precise role in the prediction of heart disease.

A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.

Mobile notifications have become a major communication channel for social networking services to keep users informed and engaged. As more mobile applications push notifications to users, they constantly face decisions on what to send, when and how. A lack of research and methodology commonly leads to heuristic decision making. Many notifications arrive at an inappropriate moment or introduce too many interruptions, failing to provide value to users and spurring users' complaints. In this paper we explore unique features of interactions between mobile notifications and user engagement. We propose a state transition framework to quantitatively evaluate the effectiveness of notifications. Within this framework, we develop a survival model for badging notifications assuming a log-linear structure and a Weibull distribution. Our results show that this model achieves more flexibility for applications and superior prediction accuracy than a logistic regression model. In particular, we provide an online use case on notification delivery time optimization to show how we make better decisions, drive more user engagement, and provide more value to users.

Predicting customer behavior, consumer demand or stock price fluctuations, identifying fraud, and diagnosing patients -- these are some of the popular applications of the random forest (RF) algorithm. Used for classification and regression tasks, it can significantly enhance the efficiency of business processes and scientific research. This blog post will cover the random forest algorithm, its operating principles, capabilities and limitations, and real-world applications. A random forest is a supervised machine learning algorithm in which the calculations of numerous decision trees are combined to produce one final result. It's popular because it is simple yet effective. Random forest is an ensemble method -- a technique where we take many base-level models and combine them to get improved results.

We live in quite an exciting time as we see technologies around us developing at a breakneck speed. We are also seeing data overtaking crude oil as the most valuable resource available for this generation's businesses. The transition of computing power from traditional on-premise mainframe data centres to easy-to-use and ever-scalable cloud computing has unlocked limitless possibilities to use data like never before. And we are just getting started to harness the true power of data. Data Science is probably the most famous buzzword in the domain of technology and IT right now. We are seeing the democratization of several tools and techniques working in tandem with the boost in computing. Using the data on almost everything, we can enable computers to learn and replicate actions as humans do.

Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in the variance of the forecast. In large scale applications of the model with millions of data points and hundreds of features, the size of the fitted objects can get very large and reach the limits on the available space in production setups, depending on the number and depth of the trees. This could be especially challenging when trained models need to be downloaded on-demand to small devices with limited memory. There is a need to approximate the trained RF models to significantly reduce the model size without losing too much of prediction accuracy. In this project we study methods that approximate each fitted tree in the Random Forests model using the multinomial allocation of the data points to the leafs. Specifically, we begin by studying whether fitting a multinomial logistic regression (and subsequently, a generalized additive model (GAM) extension) to the output of each tree helps reduce the size while preserving the prediction quality.