Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

This article was published as a part of the Data Science Blogathon. Random Forests are always referred to as black-box models. Let's try to crack open it and see what is inside it. Oops!!! Our plane has crashed, but fortunately, we all are safe. We are Data scientists, so we want to open the black box and see what random things have been recorded inside it. Yes, let's come to our topic.

This article was published as a part of the Data Science Blogathon. One of the most used matrices for measuring model performance is predictive errors. The components of any predictive errors are Noise, Bias, and Variance. This article intends to measure the bias and variance of a given model and observe the behavior of bias and variance w.r.t various models such as Linear Regression, Decision Tree, Bagging, and Random Forest for a given number of sample sizes. Bias: Difference between the prediction of the true model and the average models (models build on n number of samples obtained from the population).

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual's intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

Autonomous discovery of discrete symbols and rules from continuous interaction experience is a crucial building block of robot AI, but remains a challenging problem. Solving it will overcome the limitations in scalability, flexibility, and robustness of manually-designed symbols and rules, and will constitute a substantial advance towards autonomous robots that can learn and reason at abstract levels in open-ended environments. Towards this goal, we propose a novel and general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them that can be used in complex action planning. Our robot interacts with single and multiple objects using a given action repertoire and observes the effects created in the environment. In order to form action-grounded object, effect, and relational categories, we employ a binarized bottleneck layer of a predictive, deep encoder-decoder network that takes as input the image of the scene and the action applied, and generates the resulting object displacements in the scene (action effects) in pixel coordinates. The binary latent vector represents a learned, action-driven categorization of objects. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, we train a decision tree to reproduce its decoder function. From its branches we extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf planners to operate on the robot's sensorimotor experience. Our system is verified in a physics-based 3d simulation environment where a robot arm-hand system learned symbols that can be interpreted as 'rollable', 'insertable', 'larger-than' from its push and stack actions; and generated effective plans to achieve goals such as building towers from given cubes, balls, and cups using off-the-shelf probabilistic planners.

Building a highly accurate prediction model is certainly a difficult task. Noise – Irreducible error i.e. the part of target value which the model is not able to predict / explain. As you know it is impossible to reduce the noise, hence the term irreducible error, we shift our focus on reducing Bias and Variance. So, Ensemble learning methods bring up the technique to reduce the Bias and Variance of the model by using multiple models together (hence the term Ensemble), in order to achieve better predictive performance, instead of a single model for prediction. There are a number of Ensemble methods, in this article I will be discussing about the two widely used Ensemble methods that are Bagging and Boosting. When we use different / single learning algorithm, multiple times for prediction.

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features.

In a recent paper, Celis et al. (2020) introduced a new approach to fairness that corrects the data distribution itself. The approach is computationally appealing, but its approximation guarantees with respect to the target distribution can be quite loose as they need to rely on a (typically limited) number of constraints on data-based aggregated statistics; also resulting on a fairness guarantee which can be data dependent. Our paper makes use of a mathematical object recently introduced in privacy -- mollifiers of distributions -- and a popular approach to machine learning -- boosting -- to get an approach in the same lineage as Celis et al. but without those impediments, including in particular, better guarantees in terms of accuracy and finer guarantees in terms of fairness. The approach involves learning the sufficient statistics of an exponential family. When training data is tabular, it is defined by decision trees whose interpretability can provide clues on the source of (un)fairness. Experiments display the quality of the results obtained for simulated and real-world data.

Creating a decision tree in Python is a topic that raises a lot of questions for a beginner. What exactly is it, and what do we use it for? Where do we start building one, and what first steps do we take? Why do we use Python? Let's begin at the top. Simply put, a Python decision tree is a machine-learning method that we use for classification.

This tutorial's code is available on Github and its full implementation as well on Google Colab. A decision tree is a vital and popular tool for classification and prediction problems in machine learning, statistics, data mining, and machine learning [4]. It describes rules that can be interpreted by humans and applied in a knowledge system such as databases. It classifies cases by commencing at the tree's root and passing through it unto a leaf node. A decision tree uses nodes and leaves to make a decision.