Showing 1 - 11 results of 11 for search '"Standard deviations"', query time: 0.07s Refine Results
  1. 1

    Machine learning for email / by Conway, Drew, Safari Books Online

    Published: O'Reilly, 2012
    Table of Contents: “…-- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Modes -- Skewness -- Thin Tails vs. …”
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  2. 2

    Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics. by Hui, Eric Goh Ming, Safari Books Online

    Published: Apress L.P., 2018
    Table of Contents: “…Reading Data Files; Reading a CSV File; Writing a CSV File; Reading an Excel File; Writing an Excel File; Reading an SPSS File; Writing an SPSS File; Reading a JSON File; Basic Data Processing; Selecting Data; Sorting; Filtering; Removing Missing Values; Removing Duplicates; Some Basic Statistics Terms; Types of Data; Mode, Median, Mean; Mode; Median; Mean; Interquartile Range, Variance, Standard Deviation; Range; Interquartile Range; Variance; Standard Deviation; Normal Distribution; Modality; Skewness; Binomial Distribution; The summary() and str() Functions…”
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  3. 3

    Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications / by Lantz, Brett, Safari Books Online

    Published: Packt Publishing, 2013
    Table of Contents: “…Measuring spread -- quartiles and the five-number summaryVisualizing numeric variables -- boxplots; Visualizing numeric variables -- histograms; Understanding numeric data -- uniform and normal distributions; Measuring spread -- variance and standard deviation; Exploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification using Nearest Neighbors; Understanding classification using nearest neighbors.…”
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  4. 4

    Machine learning with R : discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R / by Lantz, Brett, Safari Books Online

    Published: Packt Publishing, 2015
    Table of Contents: “…Measuring spread -- variance and standard deviationExploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification Using Nearest Neighbors; Understanding nearest neighbor classification; The k-NN algorithm; Measuring similarity with distance; Choosing an appropriate k; Preparing data for use with k-NN; Why is the k-NN algorithm lazy?…”
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  5. 5

    Machine Learning with Python Cookbook : practical solutions from preprocessing to deep learning / by Gallatin, Kyle, Safari Books Online

    Table of Contents: “…Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem…”
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  6. 6

    Mastering .NET machine learning : master the art of machine learning with .NET and gain insight into real-world applications / by Dixon, Jamie, Safari Books Online

    Published: Packt Publishing, 2016
    Table of Contents: “…; Getting ready for machine learning; Setting up Visual Studio; Learning F#; Third-party libraries; Math.NET; Accord.NET; Numl; Summary; Chapter 2: AdventureWorks Regression; Simple linear regression; Setting up the environment; Preparing the test data; Standard deviation…”
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  7. 7

    Trends of data science and applications : theory and practices / by SpringerLink (Online service)

    Published: Springer, 2021
    Table of Contents: “…5 Measures of Central Tendency -- 5.1 Mean -- 5.2 Median -- 5.3 Mode -- 5.4 Variance -- 5.5 Standard Deviation -- 6 Distributions in Statistics -- 6.1 Probability Distributions -- 6.2 PMF Versus PDF -- 6.3 Common Probability Distributions -- 6.4 Kurtosis -- 6.5 Skewness in Distributions -- 6.6 Scaling and Transformations -- 7 Outlier Treatment -- 7.1 Understanding Outliers -- 7.2 Detecting Outliers -- 8 Correlation Analysis -- 8.1 Steps for Correlation Analysis -- 8.2 Autocorrelation Versus Partial Correlation -- 9 Variance and Covariance Analysis -- 9.1 Analysis of Variance (ANOVA)…”
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  8. 8

    Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks / by Mund, Sumit, Safari Books Online

    Published: Packt Publishing, 2015
    Table of Contents: “…Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- Introduction to predictive analytics -- Problem definition and scoping -- Data collection -- Data exploration and preparation -- Model development -- Model deployment -- Machine learning -- Kinds of machine learning problems -- Classification -- Regression -- Clustering -- Common machine learning techniques/algorithms -- Linear regression -- Logistic regression -- Decision tree-based ensemble models -- Neural networks and deep learning -- Introduction to Azure Machine Learning -- ML Studio -- Summary -- Chapter 2: ML Studio Inside Out -- Introduction to ML Studio -- Getting started with Microsoft Azure -- Microsoft account and subscription -- Creating and managing ML workspaces -- Inside ML Studio -- Experiments -- Creating and editing an experiment -- Running an experiment -- Creating and running an experiment -- do it yourself -- Workspace as a collaborative environment -- Summary -- Chapter 3: Data Exploration and Visualization -- The basic concepts -- The mean -- The median -- Standard deviation and variance -- Understanding a histogram -- The box and whiskers plot -- The outliers -- A scatter plot -- Data exploration in ML Studio -- Visualizing an automobile price dataset -- A histogram -- The box and whiskers plot -- Comparing features -- A snapshot -- Do it yourself -- Summary -- Chapter 4: Getting Data in and out of ML Studio -- Getting data in ML Studio -- Uploading data from a PC -- The Enter Data module -- The Data Reader module -- Getting data from the Web -- Getting data from Azure -- Data format conversion -- Getting data from ML Studio -- Saving dataset in a PC -- Saving results in ML Studio -- The Writer module -- Summary -- Chapter 5: Data Preparation.…”
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