Here, we load the chocolate data into our program using pandas; we also drop two of the columns we won't be using in our calculation: competitorname and winpercent. Our y becomes the first column in the dataset which indicates if our specific sweet is chocolate (1) or not (0). The remaining columns are used as variables/features to predict our y and, thus, become our X. If you're confused about why we're doing with …[:, 0][:,np.newaxis] on line 5, this is to turn y into a column. We simply add a new dimension to convert the horizontal vector into a vertical column!
There has been a lot of talk about making machine learning more explainable so that the stakeholders or the customers can shed the scepticism regarding the traditional black-box methodology. So, in order to find out how it is being implemented, a group of researchers conducted a survey. In the next section, we look at a few findings and practices for deploying as recommended by the researchers at Carnegie Mellon University, who published a work in collaboration with top institutes. During their survey, the researchers have come across some concerns such as model debugging, model monitoring and transparency among many others during the interviews that they have conducted with organisations as part of their work. The study found that most data scientists struggle with debugging poor model performance.
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models. This document contains introductory level linear algebra notes for applied machine learning. It is meant as a reference rather than a comprehensive review. It also a good introduction for people that don't need a deep understanding of linear algebra, but still want to learn about the fundamentals to read about machine learning or to use pre-packaged machine learning solutions. Further, it is a good source for people that learned linear algebra a while ago and need a refresher. These notes are based in a series of (mostly) freely ...
This lecture discusses how decision trees can be used to represent predictor functions. Variations of the basic decision tree model provide some of the most powerful machine learning methods curren... Alexander Jung uploaded a video 1 week ago Classification Methods - Duration: 46 minutes. Our focus is on linear regression methods which can be expanded by feature constructions. Guest lecture of Prof. Minna Huotilainen on learning processes in human brains. Alexander Jung subscribed to a channel 3 weeks ago Playing For Change - Channel PFC is a movement created to inspire and connect the world through music. The idea for this project came from a common belief that music has the power to break down boundaries and overcome distances SubscribeSubscribedUnsubscribe1.9M This video explains how network Lasso can be used to learn localized linear models that allow "personalized" predictions for individual data points within a network.
In this SAS How To Tutorial, Christa Cody provides an introduction to logistic regression and looks at how to perform logistic regression in SAS. After a brief introduction, she will show how to do some basic procedures to your data and fitting the model in SAS Studio. Finally, Christa will demo how to do similar tasks using SAS Model Studio. Download Data Files Download the HMEQ data set that Christa uses http://support.sas.com/documentation/... Content Outline 00:23 – Intro to Logistic Regression 04:52 – Fit the model in SAS Studio 11:31 – Show similar tasks in SAS Model Studio 12:41 – Why use logistic regression? The LOGISTIC Procedure – http://support.sas.com/documentation/... Beyond Binary Outcomes paper – http://support.sas.com/resources/pape... Free Statistics 1 e-Course – https://support.sas.com/edu/schedules... Free Intro to Statistical Concepts e-Course – https://support.sas.com/edu/schedules... Statistical Analysis learning path – http://support.sas.com/training/us/pa... SAS Tutorials on Logistic Regression – https://video.sas.com/detail/video/57... SUBSCRIBE TO THE SAS USERS YOUTUBE CHANNEL #SASUsers #LearnSAS https://www.youtube.com/SASUsers?sub_... ABOUT SAS SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data.
If I asked you to name the objects in the picture below, you would probably come up with a list of words such as "tablecloth, basket, grass, boy, girl, man, woman, orange juice bottle, tomatoes, lettuce, disposable plates…" without thinking twice. Now, if I told you to describe the picture below, you would probably say, "It's the picture of a family picnic" again without giving it a second thought. Those are two very easy tasks that any person with below-average intelligence and above the age of six or seven could accomplish. However, in the background, a very complicated process takes place. The human vision is a very intricate piece of organic technology that involves our eyes and visual cortex, but also takes into account our mental models of objects, our abstract understanding of concepts and our personal experiences through billions and trillions of interactions we've made with the world in our lives.
The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. Although there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well. We interpret our results in the contexts of learning greedy heuristics, instance feature-based algorithm selection, and parameter tuning in machine learning. Rigorously comparing algorithms is hard. Two different algorithms for a computational problem generally have incomparable performance: one algorithm is better on some inputs but worse on the others. The simplest and most common solution in the theoretical analysis of algorithms is to summarize the performance of an algorithm using a single number, such as its worst-case performance or its average-case performance with respect to an input distribution. This approach effectively advocates using the algorithm with the best summarizing value (e.g., the smallest worst-case running time). Solving a problem "in practice" generally means identifying an algorithm that works well for most or all instances of interest. When the "instances of interest" are easy to specify formally in advance--say, planar graphs, the traditional analysis approaches often give accurate performance predictions and identify useful algorithms.