Machine Learning
  • Introduction
  • Self LEarning
  • Why Statstics in ML Or Data Science
  • How important is interpretability for a model in Machine Learning?
  • What are the most important machine learning techniques to master at this time?
  • Learning
    • Supervised Learning
      • Evaluating supervised learning
        • K-fold cross validation
        • Using train/test to prevent overfitting of a polynomial regression
      • Regression
        • Linear regression
          • The ordinary least squares technique
          • The gradient descent technique
          • The co-efficient of determination or r-squared
            • Computing r-squared
            • Interpreting r-squared
          • Assumptions of linear regression
          • Steps applied in linear regression modeling
          • Evaluation Metrics Linear Regression
          • p-value
        • Ridge regression
        • Least absolute shrinkage and selection operator (lasso) Regression
        • Polynomial regression
        • Performance Metrics
        • Regularization parameters in linear regression and ridge/lasso regression
        • Comments
      • Classification
        • test
        • Logistic Regression
        • naïve Bayes
        • support vector machines (SVM)
        • decision trees
          • Split Candidates
          • Stopping conditions
          • Parameters
            • Non Tunable Or Specificable
            • Tunable
            • Stopping Parameters
        • Evaluation Metrics
      • Random Forest
        • Logistic Regression Versus Random Forest
        • Paramters
          • Non Tunable Parameters
          • Tunable
          • Stopping Param
        • Parameter Comparison of Decision Trees and Random Forests
        • Classification and Regression Trees (CART)
        • How random forest works
        • Terminologies related to random forest algorithm
        • Out-of-Bag Error
      • Decision Trees
        • Gini Index
    • Unsupervised learning
      • Clustering
        • test
        • KMeans Clustering
          • Params
          • Functions
        • Gaussian Mixture
          • Parameters
          • functions
    • Semi-supervised learning
    • Reinforcement learning
    • Learning Means What
    • Goal
    • evaluation metrics
      • Regression
        • MSE And Root Mean Squared Error (RMSE)
        • Mean Absolute Error (MAE)
      • Model Validation
        • test
      • The bias, variance, and regularization properties
        • Regularization
          • Ridge regression
        • Bias And Variance
      • The key metrics to focus
    • hyperparameters
  • Steps in machine learning model development and deployment
  • Statistical fundamentals and terminology
  • Statistics
    • Measuring Central Tendency
    • Probability
    • Standard Deviation , Variance
    • root mean squared error (RMSE)
    • mean Absolute Error
    • explained Variance
    • Coefficient of determination R2
    • Standard Error
    • Random Variable
      • Discrete
      • Continuous
    • Sample vs Population
    • Normal Distribution
    • Z Score
    • Percentile
    • Skewness and Kurtosis
    • Co-variance vs Correlation
    • Confusion matrix
    • References
    • Types of data
      • Numerical data
        • Discrete data
        • Continuous data
      • Categorical data
      • Ordinal data
    • Bias versus variance trade-off
  • Spark MLib
    • Data Types
      • Vector
      • LabeledPoint
      • Rating
      • Matrices
        • Local Matrix
        • Distributed matrix
          • RowMatrix
          • IndexedRowMatrix
          • CoordinateMatrix
          • BlockMatrix
    • Comparing algorithms supported by MLlib
      • Classification
    • When and why should you use MLlib (versus scikit-learn versus TensorFlow versus foo package)
    • Pipeline
    • References
    • Linear algebra in Spark
  • Terminology
  • Machine Learning Steps
    • test
  • Preprocessing and Feature selection techniues
  • The importance of variables feature selection/attribute selection
    • Feature Selection
      • forward selection
      • mixed selection or bidirectional elimination
      • backward selection or backward elimination
      • The key metrics to focus on
  • Feature engineering
  • Hyperplanes
  • cross-validation
  • Machine learning losses
  • When to stop tuning machine learning models
  • Train, validation, and test data
  • input data structure
  • Why are matrices/vectors used in machine learning/data analysis?
    • Linear Algebra
  • OverView
  • Data scaling and normalization
  • Questions
  • Which machine learning algorithm should I use?
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  1. Spark MLib

Data Types

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Last updated 5 years ago

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Apache Spark 2.x Machine Learning Cookbook

Sparse and dense vectors and matrices

Spark supports sparse and dense vectors and matrices. A vector or matrix is sparse if it contains mostly zeros. It’s more efficient to represent such data with pairs of indices and values at those indices. A sparse vector or matrix can be likened to a map (or dictionary in Python).

Conversely, a dense vector or matrix contains all the data—values at all index positions not storing the indices, similar to an array or a list.

From Spark In Action

Matrices and vectors in Spark can be manipulated locally (in the driver or executor processes) or in a distributed manner. Implementations of distributed matrices in Spark enable you to perform linear algebra operations on huge amounts of data, spanning numerous machines. For local linear algebra operations, Spark uses the very fast Breeze and jblas libraries (and NumPy in Python), and it has its own implementations of distributed ones.