In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks $\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve the query complexity of both algorithms. We give a randomized polynomial time algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$ queries.

AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. NOTE: This video assumes you already know about Decision Trees... https://youtu.be/7VeUPuFGJHk Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer

You've come a long way from writing your first line of Python or R code. You know your way around Scikit-Learn like the back of your hand. You spend more time on Kaggle than Facebook now. You're no stranger to building awesome random forests and other tree based ensemble models that get the job done. You want to dig deeper and understand some of the intricacies and concepts behind popular machine learning models.

Imagine you are out to buy a cell phone for yourself. Shopkeeper asks,"How can I help you Ma'am?" "I am looking for a cell phone" "You are at the right place, we have over 300 different types of cell phones, what kind of phone would you like to buy today?" Decision paralysis hits you, totally confused among so many choices of phones you go blank! "Let me help you choose a phone ma'am. What screen size would you like?" "Umm… larger than 5.9 inches" "Perfect, and how about the camera?"

In this tutorial a small introduction of machine learning focused on development will be done with one of the most used Java libraries for this purpose, Weka. The machine learning is a subfield of data science . If data science covers the entire process of obtaining knowledge, cleaning, analysis, visualization and data deployment, machine learning are the algorithms and techniques used in the analysis and modeling phase of this process. Within these, we will focus on supervised learning, which is often used for classification and regression problems. The classification can be applied when dealing with a discrete class, where the objective is to predict one of the mutually exclusive values in the target variable.

Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide very efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around smooth circular cylinders within the studied Re and Ti range.

Machine learning sure is an umbrella word for many methodologies and tools but one must be clear about the fact that it is not an umbrella word for all the solutions. No one can deny that machine learning has revolutionised the way data can be squeezed in for discoveries. What one should care about is that the enhancement of any technology also depends on a relentless introspective approach in attacking the shortcomings. The rise in popularity sure lures every amateur into believing that they have reached their destination. With tools and frameworks being open-sourced, everyone can play with data, experiment with MNIST datasets and get really good accuracy scores.

Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning model. In this paper, we compared the performance of different algorithms, first on an unpruned dataset and then on an iteratively pruned dataset. The goal was to understand whether an algorithm (say A) on an unpruned dataset performs better than another algorithm (say B), will algorithm B perform better on the pruned data or vice-versa. The dataset chosen for our analysis is a subset of the largest movie ratings database publicly available on the internet, IMDb [1]. The learning objective of the model was to predict the categorical rating of a movie among 5 bins: poor, average, good, very good, excellent. The results indicated that an algorithm that performed better on an unpruned dataset also performed better on a pruned dataset.

Noname manuscript No. (will be inserted by the editor) Abstract Bayesian Decision Trees are known for their probabilistic interpretability. However,their construction can sometimes be costly. In this article we present a general Bayesian Decision Tree algorithm applicable to both regression and classification problems. The algorithm does not apply Markov Chain Monte Carlo and does not require a pruning step. While it is possible to construct a weighted probability tree space we find that one particular tree, the greedy-modal tree (GMT), explains most of the information contained in the numerical examples. This approach seems to perform similarly to Random Forests. KeywordsMachine learning · Bayesian statistics · Decision Trees · Random Forests 1 Introduction Decision trees are popular machine learning techniques applied to both classification andregression tasks.

Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.