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. Statistics
  2. Types of data

Ordinal data

PreviousCategorical dataNextBias versus variance trade-off

Last updated 5 years ago

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The last category that you tend to hear about with types of data is ordinal data, and it's sort of a mixture of numerical and categorical data. A common example is star ratings for a movie or music, or what have you.

we have categorical data in that could be 1 through 5 stars, where 1 might represent poor and 5 might represent excellent, but they do have mathematical meaning. We do know that 5 means it's better than a 1, so this is a case where we have data where the different categories have a numerical relationship to each other. So, I can say that 1 star is less than 5 stars, I can say that 2 stars is less than 3 stars, I can say that 4 stars is greater than 2 stars in terms of a measure of quality. Now you could also think of the actual number of stars as discrete numerical data. So, it's definitely a fine line between these categories, and in a lot of cases you can actually treat them interchangeably.

So, there you have it, the three different types. There is numerical, categorical, and ordinal data. Let's see if it's sunk in. Don't worry, I'm not going to make you hand in your work or anything.