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Search results for Probability Statistics

    Found 57 documents, 11278 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

  • Before Probability Distributions

    ...on parameter can be estimated from a sample (inference)   So, how do we infer? The possible outcomes of a random variable can be estimated using statistics and probability in different ways: Calculating the value of a single outcome of a random variable (like the exact value of a stock price...

    https://www.kdnuggets.com/2020/07/before-probability-distributions.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

  • The Data Science Interview Study Guide

    ...Conditional Prob Article Probability Quiz Probability & Statistics — Set 6 Probability & Statistics — Set 2 Independent Probability Dependent Probability Probability Interview Questions Most of these questions are either similar to the ones we have been asked or taken directly from...

    https://www.kdnuggets.com/2020/01/data-science-interview-study-guide.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

  • The 8 Basic Statistics Concepts for Data Science

    ...ytics provides recommendations regarding actions that will take advantage of the predictions and guide the possible actions toward a solution.   Probability Probability is the measure of the likelihood that an event will occur in a Random Experiment. Complement: P(A) + P(A’) = 1...

    https://www.kdnuggets.com/2020/06/8-basic-statistics-concepts.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

  • A Complete Guide To Survival Analysis In Python, part 2

    ...person. Like they survived the 1st, 2nd, and 3rd timeslines, then our survival probability will be: Getting back to our main example: (14) Surv_After_probability: We want to find the probability that a patient has survived through all the timeline till now. Now we need to find the actual survival...

    https://www.kdnuggets.com/2020/07/guide-survival-analysis-python-part-2.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

  • Probability Distributions in Data Science">Silver BlogProbability Distributions in Data Science

    ...ights). From discrete random variables, it is possible to calculate Probability Mass Functions, while from continuous random variables can be derived Probability Density Functions. Probability Mass Functions gives the probability that a variable can be equal to a certain value, instead, the values...

    https://www.kdnuggets.com/2020/02/probability-distributions-data-science.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

  • Overview of data distributions">Silver BlogOverview of data distributions

    ...s the conjugate prior of the categorical and multinomial distribution. It is parameterized by a vector alpha of positive reals, and it samples over a probability simplex. A probability simplex is a set of k numbers adding up to 1 and which correspond to the probabilities of k classes. A...

    https://www.kdnuggets.com/2020/06/overview-data-distributions.html

  • 4 Free Math Courses to do and Level up your Data Science Skills">Silver Blog4 Free Math Courses to do and Level up your Data Science Skills

    ...of what you learned in the first place? You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization, and many would-be AI practitioners find this daunting. This course is not designed to make you a...

    https://www.kdnuggets.com/2020/06/4-free-maths-courses.html

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

  • Statistics with Julia: The Free eBook">Silver BlogStatistics with Julia: The Free eBook

    ...hese ends up being no, making this a resource truly befitting a beginner. The book's table of contents, including appendices: Introducing Julia Basic Probability Probability Distributions Processing and Summarizing Data Statistical Inference Concepts Confidence Intervals Hypothesis Testing Linear...

    https://www.kdnuggets.com/2020/09/statistics-julia-free-ebook.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

  • Free Mathematics Courses for Data Science & Machine Learning">Gold BlogFree Mathematics Courses for Data Science & Machine Learning

    ...me striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational mathematics.   Statistics & Probability Statistics and probability are the foundations of data science, more so than any other family of mathematical concepts. These courses...

    https://www.kdnuggets.com/2020/02/free-mathematics-courses-data-science-machine-learning.html

  • A Complete Guide To Survival Analysis In Python, part 1">Silver BlogA Complete Guide To Survival Analysis In Python, part 1

    ...ability (2) The hazard probability To find survival probability, we’ll be using survivor function S(t), which is the Kaplan-Meier Estimator. Survival probability is the probability that an individual (e.g., patient) survives from the time origin (e.g., diagnosis of cancer) to a specified future...

    https://www.kdnuggets.com/2020/07/complete-guide-survival-analysis-python-part1.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

  • Time Series Classification Synthetic vs Real Financial Time Series">Silver BlogTime Series Classification Synthetic vs Real Financial Time Series

    ..."dashed", size = 1) + geom_histogram(aes(y = ..density..), colour = "black", fill = "white", alpha = 0.1, position = "identity") + ggtitle("Predicted probability density plot") + theme_tq() # The average predicted probability sits around 0.48 / 0.49, for simplicity I will just select 0.50 as the...

    https://www.kdnuggets.com/2020/03/time-series-classification-synthetic-real-financial-time-series.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

  • 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

  • Looking Normal(ly Distributed)

    ...t, it seems that the normal distribution makes for a pretty good hammer.   Bio: Gregory Janesch (LinkedIn) is currently working on a Master's in Statistics (finishing in December 2020) in pursuit of a data scientist or statistician role. Original. Reposted with permission. Related: Probability...

    https://www.kdnuggets.com/2020/05/looking-normally-distributed.html

  • Demystifying Statistical Significance

    ...obability of observing a difference in proportions (or means, depending on the parameter you’ve selected for your test). The p-value is a conditional probability, which means that we are getting the probability given that a specific condition is true. Formally, we can say that the p-value is the...

    https://www.kdnuggets.com/2020/07/demystifying-statistical-significance.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

  • Data Science Internship Interview Questions

    ...blems and practice case studies that you can find online. Statistics & Probability Image from Unsplash. You should have an understanding of basic statistics and probability. These concepts serve as the base for most machine learning and data science concepts. As well, many of the interview...

    https://www.kdnuggets.com/2020/08/data-science-internship-interview-questions.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

  • 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

  • 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

  • A Complete Guide To Survival Analysis In Python, part 3

    ...Female. (7) Predicting survival probabilities: Now we can predict the survival probability for both the groups. (8) Get the complete list of survival_probability: (9) Plot the graph: Notice that the probability of a female surviving lung cancer is higher than the probability of a male surviving...

    https://www.kdnuggets.com/2020/07/guide-survival-analysis-python-part-3.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

  • Model Evaluation Metrics in Machine Learning">Silver BlogModel Evaluation Metrics in Machine Learning

    ...rt these class outputs to probability. Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc. give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.   Introduction  ...

    https://www.kdnuggets.com/2020/05/model-evaluation-metrics-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

  • 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

  • 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

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