Search results for Probability Statistics

    Found 58 documents, 10397 searched:

  • Probability Mass and Density Functions

    ...to different properties for the probability density function: In this case, p(x) is not necessarily less than 1 because it doesn’t correspond to the probability (the probability itself will still need to be between 0 and 1).   Example 5.   For instance, let’s say that we have a...

    https://www.kdnuggets.com/2019/05/probability-mass-density-functions.html

  • Basic Statistics in Python: Probability

    ...data! We can use statistics to calculate probabilities based on observations from the real world and check how it compares to the ideal.   From statistics to probability   Our data will be generated by flipping a coin 10 times and counting how many times we get heads. We will call a set...

    https://www.kdnuggets.com/2018/08/basic-statistics-python-probability.html

  • Naive Bayes: A Baseline Model for Machine Learning Classification Performance

    ...parts of Bayes Theorem: P(A|B) - Posterior Probability The conditional probability that event A occurs given that event B has occurred. P(A) - Prior Probability The probability of event A. P(B) - Evidence The probability of event B. P(B|A) - Likelihood The conditional probability of B occurring...

    https://www.kdnuggets.com/2019/04/naive-bayes-baseline-model-machine-learning-classification-performance.html

  • Probability Learning: Bayes’ Theorem

    ...ositive (E) in a test for such disease, which is what we actually want to calculate. The vertical bars (|) in a probability term denote a conditional probability (ie, the probability of A given B would be P(A|B)). The left term of the numerator on the right side P(E|H) is the probability of the...

    https://www.kdnuggets.com/2019/10/probability-learning-bayes-theorem.html

  • 5 Probability Distributions Every Data Scientist Should Know">Gold Blog5 Probability Distributions Every Data Scientist Should Know

    ...s Aires University, and a data scientist at MercadoLibre. He also writes about machine learning and data on www.datastuff.tech. Original. Reposted with permission. Related: Data Science Basics: Power Laws and Distributions Basic Statistics in Python: Probability Probability Mass and Density...

    https://www.kdnuggets.com/2019/07/5-probability-distributions-every-data-scientist-should-know.html

  • Unfolding Naive Bayes From Scratch

    ...e probabilistic scores! Step # 3 : Using Probability to Predict Label for Tokenized Test Example The not so intimidating mathematical form of finding probability Probability of a Given Test Example i of belonging to class c let i = test example = “Very good food and service!!!” Total number of...

    https://www.kdnuggets.com/2018/09/unfolding-naive-bayes.html

  • How Bayesian Inference Works">Gold BlogHow Bayesian Inference Works

    ...The posterior, P(w | m), shows the probability of Reign being a given weight, given the measurements we made. This is what we are most interested in. Probability of data, P(m), shows the probability that any given data point will be measured. For now we’ll assume this is a constant, that is, that...

    https://www.kdnuggets.com/2016/11/how-bayesian-inference-works.html

  • Bayes Theorem for Computer Scientists, Explained

    …n. This article aims to clear up some foundational concepts in probability (and, briefly, how they apply to computer science) as quickly as possible. Probability Theory What? Probability theory is a branch of mathematics concerned with random processes (also known as stochastic processes). Why?…

    https://www.kdnuggets.com/2016/02/bayes-theorem-computer-scientists-explained.html

  • Beta Distribution: What, When & How

    ...ence world), beta distribution can be used to represent all the possible values that probability can take. Thanks to wikipedia for the definition. In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1]...

    https://www.kdnuggets.com/2019/09/beta-distribution-what-when-how.html

  • The 5 Basic Statistics Concepts Data Scientists Need to Know">Silver BlogThe 5 Basic Statistics Concepts Data Scientists Need to Know

    ...g any more data!   Bayesian Statistics   Fully understanding why we use Bayesian Statistics requires us to first understand where Frequency Statistics fails. Frequency Statistics is the type of stats that most people think about when they hear the word “probability”. It involves applying...

    https://www.kdnuggets.com/2018/11/5-basic-statistics-concepts-data-scientists-need-know.html

  • How I Learned to Stop Worrying and Love Uncertainty

    ...y is to account for uncertainty, measuring and presenting it instead of reducing and hiding it, and a great framework to do this is known as Bayesian Statistics. Bayesian Statistics Bayesianism is rooted in the idea that probability is a measure of uncertainty and, as such, it is dependent on the...

    https://www.kdnuggets.com/2018/10/stop-worrying-love-uncertainty.html

  • Bayesian Basics, Explained">Silver BlogBayesian Basics, Explained

    ...efly explain in layperson's terms what it is and how it differs from the 'ordinary' statistics most of us learned in college? Andrew Gelman: Bayesian statistics uses the mathematical rules of probability to combines data with “prior information” to give inferences which (if the model being used is...

    https://www.kdnuggets.com/2016/12/bayesian-basics-explained.html

  • How to Become a (Good) Data Scientist – Beginner Guide">Platinum BlogHow to Become a (Good) Data Scientist – Beginner Guide

    ...nce — an online course for beginners; Business Analytics Fundamentals — another introductory course teaching the basic concepts of BI. Statistics and probability Probability and statistics are the basis of Data Science. Statistics is, in simple terms, the use of mathematics to perform technical...

    https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html

  • Best Data Science Online Courses

    …Data Science with R Derek Kane | Data Science MarinStatsLectures | Statistics LearnR | R programming Christoph Scherber | Statistics Brandon Foltz | Statistics statisticsfun | Statistics Java and R Tutorials | R programming bigdata simplified | All things big data Derek Banas | Playlists on SQL…

    https://www.kdnuggets.com/2015/10/best-data-science-online-courses.html

  • Learning and Teaching Machine Learning: A Personal Journey

    ...nguistics, and computer science. Class prerequisite are kept fairly minimal; those consist of the standard upper division undergraduate coursework in probability, statistics and linear algebra, but we don’t require coursework in more advanced subjects like measure-theoretic probability,...

    https://www.kdnuggets.com/2014/04/learning-teaching-machine-learning-personal-journey.html

  • Top 10 Data Mining Algorithms, Explained

    ...1 given Class A multiplied by the probability of Feature 2 given Class A multiplied by the probability of Class A. The fraction’s denominator is the probability of Feature 1 multiplied by the probability of Feature 2. What is an example of Naive Bayes? Below is a great example taken from a Stack...

    https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

  • Probability Learning: Maximum Likelihood

    ...w, which corresponds to a height of 172 cm) is classified as female, as for that specific height value the female height distribution yields a higher probability than the male one. That’s very cool you might say, but how do we actually calculate these probability distributions? Do not worry, we...

    https://www.kdnuggets.com/2019/11/probability-learning-maximum-likelihood.html

  • How to Become a Data Scientist: The Definitive Guide">Silver Blog, Aug 2017How to Become a Data Scientist: The Definitive Guide

    ...greater.” — Albert Einstein The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core...

    https://www.kdnuggets.com/2017/08/become-data-scientist-definitive-guide.html

  • Modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models

    ...y sized groups with the parameter ntiles. Hence, ntiles=100 results in 100 equally sized groups with in the first group the 1% with the highest model probability and in group 100 the 1% with the lowest model probability. These groups are often referred to as percentiles; modelplotr will also label...

    https://www.kdnuggets.com/2019/06/modelplotr-cran-business-value-predictive-models.html

  • A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more

    ...chine Learning Course. Matrix Calc for DL (pdf here) Really nice overview of matrix calculus for deep learning from Parr/Howard. Citable on on arxiv. Probability and Statistics File Description Seeing Theory Frequentist Inference This is a really beautiful visual presentation of the basic ideas of...

    https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html

  • Explaining the 68-95-99.7 rule for a Normal Distribution">Silver BlogExplaining the 68-95-99.7 rule for a Normal Distribution

    ...x.set_ylim(0); ax.set_title('Normal Distribution', size = 20); ax.set_ylabel('Probability Density', size = 20); The graph above does not show you the probability of events but their probability density. To get the probability of an event within a given range we will need to integrate. Suppose we...

    https://www.kdnuggets.com/2018/07/explaining-68-95-99-7-rule-normal-distribution.html

  • Plausibility vs. probability, prior distributions, and the garden of forking paths

    ...tion has to be peaked around zero. I think there’s a theorem in there for someone who’d like to do some digging. Bio: Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. Andrew has done research on a wide range of...

    https://www.kdnuggets.com/2016/01/plausibility-probability-prior-distributions.html

  • Platinum BlogEssential Math for Data Science:  ‘Why’ and ‘How’">SilverPlatinum BlogEssential Math for Data Science:  ‘Why’ and ‘How’

    ...cs, central tendency, variance, covariance, correlation, Basic probability: basic idea, expectation, probability calculus, Bayes theorem, conditional probability, Probability distribution functions — uniform, normal, binomial, chi-square, student’s t-distribution, Central limit theorem, Sampling,...

    https://www.kdnuggets.com/2018/09/essential-math-data-science.html

  • Top 10 Big Ideas in Harvard Statistics 110 Class

    ...the Harvard Professor Joe Blitzstein, presented on the last day of Statistics 110 classes for the semester. Conditioning. Conditioning is the soul of statistics. This includes both conditional probability and conditional expectation. It includes ideas such as Bayes' rule, the law of total...

    https://www.kdnuggets.com/2013/12/top-10-big-ideas-harvard-statistics-110-class.html

  • A Primer on Logistic Regression – Part I

    ...will churn out. Note that odds can be converted back into probability as In common sense, probability and odds are used interchangeably. However, in statistics, probability and odds are not the same, but different. The dataset (with these relevant terms) is displayed below, which forms the basis...

    https://www.kdnuggets.com/2016/08/primer-logistic-regression-part-1.html

  • Evaluating the Business Value of Predictive Models in Python and R

    ...el's business value. Although each plot sheds light on the business value of your model from a different angle, they all use the same data: Predicted probability for the target class Equally sized groups based on this predicted probability Actual number of observed target class observations in...

    https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html

  • Central Limit Theorem for Data Science – Part 2

    ...1.5. This is equivalent to saying the standard deviation of the sampling distribution of the mean is 1.5. This value is essential in calculating the probability of us being wrong. Probability of an observation   Armed with the standard error, we can now calculate the probability of our...

    https://www.kdnuggets.com/2016/08/central-limit-theorem-data-science-part-2.html

  • Classification vs Prediction

    ...obabilities is that they are their own error measures. If the probability of disease is 0.1 and the current decision is not to treat the patient, the probability of this being an error is by definition 0.1. A probability of 0.4 may lead the physician to run another lab test or do a biopsy. When the...

    https://www.kdnuggets.com/2019/09/classification-prediction.html

  • A Tutorial on the Expectation Maximization (EM) Algorithm

    ...here the denominator in Equation 5 comes from. The denominator is the sum of probabilities of observing xi in each cluster weighted by that cluster’s probability. Essentially, it is the total probability of observing xi in our data. If we are making hard cluster assignments, we will take the...

    https://www.kdnuggets.com/2016/08/tutorial-expectation-maximization-algorithm.html

  • How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?">Gold BlogHow Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?

    ...value decomposition, i) eigenvalues, eigenvectors, and diagonalization. Here is a nice Medium article on what you can accomplish with linear algebra. Statistics and Probability Only death and taxes are certain, and for everything else there is normal distribution. The importance of having a solid...

    https://www.kdnuggets.com/2017/12/mathematics-needed-learn-data-science-machine-learning.html

  • Beating the Bookies with Machine Learning

    ...can try to have a machine-learning (ML) algorithm do this for us. Betting on darts with the help of ML For the purpose of this project we used darts statistics, including features such as averages, checkout percentages, number of 180s (maximum score with 3 darts) and head-to-head statistics. In...

    https://www.kdnuggets.com/2019/03/beating-bookies-machine-learning.html

  • Introduction to Bayesian Inference

    ...de some examples written in Python to help you get started. To get the most out of this introduction, the reader should have a basic understanding of statistics and probability, as well as some experience with Python. The examples use the Python package pymc3. Introduction to Bayesian Thinking...

    https://www.kdnuggets.com/2016/12/datascience-introduction-bayesian-inference.html

  • P-values Explained By Data Scientist

    ...erpret the p-values in our hypothesis testings. Hopefully the hard part now becomes at least slightly easier for you. If you want to learn more about statistics, I highly recommend you to read this book (which I’m reading it now!) — Practical Statistics for Data Scientists written specially for...

    https://www.kdnuggets.com/2019/07/p-values-explained-data-scientist.html

  • When Bayes, Ockham, and Shannon come together to define machine learning

    ...cess (which we can never observe), that is behind the generation of a random variable (which we can observe or measure, albeit not without noise). In statistics, it is generally defined as a probability distribution. But in the context of machine learning, it can be thought of any set of rules (or...

    https://www.kdnuggets.com/2018/09/when-bayes-ockham-shannon-come-together-define-machine-learning.html

  • A Gentle Introduction to Noise Contrastive Estimation

    ...mples, we can analytically calculate any particular word’s probability according to this distribution, Q. For instance, if we define “word-1” to have probability 10% and “word-2” with probability 90%, and we happen to pull a sample of “word-1”, then Q = 0.10; it’s just a reference to the...

    https://www.kdnuggets.com/2019/07/introduction-noise-contrastive-estimation.html

  • How to correctly select a sample from a huge dataset in machine learning">Silver BlogHow to correctly select a sample from a huge dataset in machine learning

    ...sume that the sample is not biased. The comparison between sample and population is then made this way: Take one variable from the sample Compare its probability distribution with the probability distribution of the same variable of the population Repeat with all the variables Some of you could...

    https://www.kdnuggets.com/2019/05/sample-huge-dataset-machine-learning.html

  • Every Intro to Data Science Course on the Internet, Ranked">Silver Blog, March 2017Every Intro to Data Science Course on the Internet, Ranked

    ...-piece series that covers the best online courses for launching yourself into the data science field. We covered programming in the first article and statistics and probability in the second article. The remainder of the series will cover other data science core competencies: data visualization and...

    https://www.kdnuggets.com/2017/03/every-intro-data-science-course-ranked.html

  • How to count Big Data: Probabilistic data structures and algorithms

    ...l University in Ukraine for a number of years and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany.   Related: 5 Probability Distributions Every Data Scientist Should Know Basic Statistics in Python:...

    https://www.kdnuggets.com/2019/08/count-big-data-probabilistic-data-structures-algorithms.html

  • Introduction to Markov Chains">Silver BlogIntroduction to Markov Chains

    ...cience. He will be a software engineering intern at Airbnb in 2018. He can be reached via LinkedIn. Original. Reposted with permission. Related: What Statistics Topics are Needed for Excelling at Data Science? Applied Statistics Is A Way Of Thinking, Not Just A Toolbox All Machine Learning Models...

    https://www.kdnuggets.com/2018/03/introduction-markov-chains.html

  • Data Science for Javascript Developers 

    ...bution is centered around 126K. We can also see that the distribution is skewed, or asymmetrical. Here’s what Wikipedia has to say about skewness: In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its...

    https://www.kdnuggets.com/2018/03/data-science-javascript-developers.html

  • Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall

    …enings to sheer randomness. The scientific antidote to this failing is probability, which Taleb affectionately dubs “a branch of applied skepticism.” Statistics is the resource we rely on to gauge probability. It answers the orange car question above by calculating the probability that what’s been…

    https://www.kdnuggets.com/2017/01/siegel-data-science-avoiding-prediction-pitfall.html

  • What Top Firms Ask: 100+ Data Science Interview Questions

    …“Good food, bad service,” your score might be 1 – 1 = 0. Spark: Capital One Data Engineer Explain how RDDs work with Scala in Spark Statistics & Probability Questions: Google Explain Cross-validation as if you’re talking to a non-technical person. Describe a non-normal probability distribution…

    https://www.kdnuggets.com/2017/03/top-firms-100-data-science-interview-questions.html

  • How Bayes’ Theorem is Applied in Machine Learning

    ...s   In case you want to go more in depth into Bayes and Machine Learning, check out these other resources: How Bayesian Inference works Bayesian statistics Youtube Series Machine Learning Bayesian Learning slides Bayesian Inference and as always, contact me with any questions. Have a fantastic...

    https://www.kdnuggets.com/2019/10/bayes-theorem-applied-machine-learning.html

  • Mathematical programming —  Key Habit to Build Up for Advancing Data Science">Gold BlogMathematical programming —  Key Habit to Build Up for Advancing Data Science

    ...al pattern emerges from a set of a large number of repeated experiments of a similar kind through their mutual interaction. Frequentist definition of probability: There are two broad categories of the definition of probability and two fiercely rival camps — frequentists and Bayesians. It is easy to...

    https://www.kdnuggets.com/2019/05/mathematical-programming-key-habit-advancing-data-science.html

  • The Foundations of Algorithmic Bias

    …hat it is spam. A simple model might be to assign a score (weight) to every word in the vocabulary. If that weight is positive, then it increases the probability that the email is spam. If negative it decreases the probability. To calculate the final score, we might sum up the counts of each word,…

    https://www.kdnuggets.com/2016/11/foundations-algorithmic-bias.html

  • Top Stories, Jul 1-7: 5 Probability Distributions Every Data Scientist Should Know; NLP vs. NLU: from Understanding a Language to Its Processing

    ...Workstation a Review and Benchmark NLP vs. NLU: from Understanding a Language to Its Processing Top 8 Data Science Use Cases in Construction 5 Useful Statistics Data Scientists Need to Know Most Shared Last Week NLP vs. NLU: from Understanding a Language to Its Processing, by Sciforce - Jul 03,...

    https://www.kdnuggets.com/2019/07/top-news-week-0701-0707.html

  • The Best Metric to Measure Accuracy of Classification Models

    …rvation Actual Predicted 1 Non-Fraud 0.45 2 Non-Fraud 0.10 3 Fraud 0.67 4 Non-Fraud 0.60 5 Non-Fraud 0.11   Suppose we assume 0.5 as the cut-off probability i.e. observations with probability value of 0.5 and above are marked as Fraud and below 0.5 are marked as Non-Fraud as shown in the table…

    https://www.kdnuggets.com/2016/12/best-metric-measure-accuracy-classification-models.html

  • Logistic Regression: A Concise Technical Overview

    ...a variable / feature / column j refers to the category level of the target variable. The baseline model logit (5 & 6) shows us that the predicted probability value is the log odds of log probability j (log(πj)) relative to the selected baseline log probability (log(π1)). Each category...

    https://www.kdnuggets.com/2019/01/logistic-regression-concise-technical-overview.html

  • Interpretability over Accuracy

    ...coefficient is not directly related to the response. “Logistic” refers to the logit, which is the log of the odds of the response (odds are equal to probability over one minus probability). So the coefficient relates to a transformation of the probability of response, and because the function is...

    https://www.kdnuggets.com/2016/08/salford-interpretability-over-accuracy.html

  • Big Data Lessons from Microsoft “how-old” Experiment

    …and generally in life, that faces are extraordinarily beautiful and complex. As an artist they are highly difficult to even draw and explain. Clearly probability and statistics have a place in cracking the riddle behind how they work. One day we might wrest control -to a robot- of quick and…

    https://www.kdnuggets.com/2015/05/face-numbers-big-data-microsoft-how-old.html

  • How (dis)similar are my train and test data?

    ...classifier. predictions[:10]----output----- array([ 0.34593171]) So for the first row our classifier thinks that it belongs to training data with .34 probability. Let’s call this P(train). Or we can also say that it has .66 probability of being from the test data. Let’s call this as P(test). Now...

    https://www.kdnuggets.com/2018/06/how-dissimilar-train-test-data.html

  • The 10 Algorithms Machine Learning Engineers Need to Know">2016 Gold BlogThe 10 Algorithms Machine Learning Engineers Need to Know

    ...ssumptions between the features. The featured image is the equation — with P(A|B) is posterior probability, P(B|A) is likelihood, P(A) is class prior probability, and P(B) is predictor prior probability. Naive Bayes Classification Some of real world examples are: To mark an email as spam or not...

    https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html

  • Lift Analysis – A Data Scientist’s Secret Weapon

    ...dt is a data scientist from Hamburg, Germany. He currently works for Akanoo, an onsite targeting startup. Previously he worked in the data team of a DIY website builder. Original. Reposted with permission. Related: Which Big Data, Data Mining, and Data Science Tools go together? Statistics Denial...

    https://www.kdnuggets.com/2016/03/lift-analysis-data-scientist-secret-weapon.html

  • Crushed it! Landing a data science job

    …hould be prepared to program, typically in a language of your choice. Easy peasy right?! Books It probably doesn’t matter which one, but get an intro probability book. I used my trusty old Ross, a standard undergrad text in probability. If you have Ross, I recommend doing the self-tests in chapters…

    https://www.kdnuggets.com/2015/10/erin-shellman-landing-data-science-job.html

  • Understanding Rare Events and Anomalies: Why streaks patterns change

    …o with rare patterns in the future. So any rare pattern can become rarer, less rare, or equally rare, going forward. And we’ll be able to construct a probability analysis that creatively bootstraps all of the past U.S. S&P data to show this. Since the start of the S&P, we have 66 years of…

    https://www.kdnuggets.com/2016/01/understanding-rare-events-anomalies-patterns-change.html

  • A Simpler Explanation of Differential Privacy

    …earning Differential privacy aims to make the answers to “snooping queries” too vague to distinguish closely related sets (in this case, it makes the probability that A(S) ≥ T about the same as the probability that A(S’) ≥ T). But for machine learning, we are also interested in the output of A()….

    https://www.kdnuggets.com/2015/11/simpler-explanation-differential-privacy.html

  • Machine Learning Key Terms, Explained

    ...uentist, probability interpretation views probabilities in terms of the frequencies of random events. In somewhat of a contrast, the Bayesian view of probability aims to quantify uncertainty, and updates a given probability as additional evidence is available. If these probabilities are extended to...

    https://www.kdnuggets.com/2016/05/machine-learning-key-terms-explained.html

  • Fundamental methods of Data Science: Classification, Regression And Similarity Matching

    ...al for classification task is given a new individual; determine which class that individual belongs to. A closely related concept is scoring or class probability estimation. A Scoring model when applied to an individual produces a score representing the probability that the individual belongs to...

    https://www.kdnuggets.com/2015/01/fundamental-methods-data-science-classification-regression-similarity-matching.html

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