• My Data Science Notes
  • Intro
  • 1 Probability
    • 1.1 Principles
    • 1.2 Discrete Distributions
      • 1.2.1 Bernoulli
      • 1.2.2 Binomial
      • 1.2.3 Poission
      • 1.2.4 Multinomial
      • 1.2.5 Negative-Binomial
      • 1.2.6 Geometric
      • 1.2.7 Hypergeometric
      • 1.2.8 Gamma
    • 1.3 Continuous Distributions
      • 1.3.1 Normal
    • 1.4 Join Distributions
    • 1.5 Likelihood
  • 2 Statistical Tests
    • 2.1 Chi-Square Test
    • 2.2 One-Way Tables
      • 2.2.1 Chi-Square Goodness-of-Fit Test
      • 2.2.2 Proportion Test
    • 2.3 Two-Way Tables
      • 2.3.1 Chi-Square Independence Test
      • 2.3.2 Residuals Analysis
      • 2.3.3 Difference in Proportions
      • 2.3.4 Relative Risk
      • 2.3.5 Odds Ratio
      • 2.3.6 Partitioning Chi-Square
      • 2.3.7 Correlation
    • 2.4 K-Way Tables
      • 2.4.1 Odds Ratio
      • 2.4.2 Chi-Square Independence Test
    • 2.5 Continuous Variable Analysis
      • 2.5.1 Correlation
  • 3 Experiment Design
    • 3.1 Single Factor
    • 3.2 Blocking
    • 3.3 Nested
    • 3.4 Split Plot
  • PART 2: Supervised Machine Learning
  • 4 Ordinary Least Squares
    • 4.1 Linear Regression Model
    • 4.2 Parameter Estimation
    • 4.3 Model Assumptions
      • 4.3.1 Linearity
      • 4.3.2 Multicollinearity
      • 4.3.3 Normality
      • 4.3.4 Equal Variances
    • 4.4 Prediction
    • 4.5 Inference
      • 4.5.1 t-Test
      • 4.5.2 F-Test
    • 4.6 Interpretation
    • 4.7 Model Validation
      • 4.7.1 Accuracy Metrics
      • 4.7.2 Cross-Validation
      • 4.7.3 Gain Curve
    • 4.8 OLS Reference
  • 5 Generalized Linear Models
    • 5.1 Logistic Regression
    • 5.2 Multinomial Logistic Regression
    • 5.3 Ordinal Logistic Regression
      • 5.3.1 Assumptions
      • 5.3.2 Modeling
      • 5.3.3 Case Study
    • 5.4 Poisson Regression
  • 6 Multivariate Statistical Analysis
    • 6.1 Background
    • 6.2 MANOVA
    • 6.3 Repeated Measures
    • 6.4 LDA
  • 7 Regularization
    • 7.1 Ridge
    • 7.2 Lasso
    • 7.3 Elastic Net
    • Model Summary
  • 8 Decision Trees
    • 8.1 Classification Tree
      • 8.1.1 Measuring Performance
      • 8.1.2 Training with Caret
    • 8.2 Regression Tree
      • 8.2.1 Training with Caret
    • 8.3 Bagged Trees
      • 8.3.1 Bagged Classification Tree
      • 8.3.2 Bagging Regression Tree
    • 8.4 Random Forests
    • 8.5 Gradient Boosting
    • 8.6 Summary
      • 8.6.1 Classification Trees
      • 8.6.2 Regression Trees
  • 9 Non-linear Models
    • 9.1 Splines
    • 9.2 MARS
    • 9.3 GAM
  • 10 Support Vector Machines
    • 10.1 Maximal Margin Classifier
    • 10.2 Support Vector Classifier
    • 10.3 Support Vector Machines
    • 10.4 My svm Example
    • 10.5 Using Caret
  • PART 3: Unupervised Machine Learning
  • 11 Dimensionality Reduction
    • 11.1 PCA
    • 11.2 t-SNE
    • 11.3 SVD
  • 12 Cluster Analysis
    • 12.1 K-Means
    • 12.2 HCA
  • 13 Text Mining
    • 13.1 Tidy Text
    • 13.2 Bag of Words
    • 13.3 Sentiment Analysis
      • 13.3.1 N-Grams
      • 13.3.2 Converting to and from non-tidy formats
      • 13.3.3 Example
    • 13.4 Topic Modeling
    • 13.5 Appendix: String Manipulation
      • 13.5.1 stringr package
      • 13.5.2 Regular Expressions
    • 13.6 Reference Links
  • 14 Survival Analysis
    • 14.1 Basic Concepts
    • 14.2 Survival Curve Estimation
      • 14.2.1 Kaplan-Meier
      • 14.2.2 Weibull
      • 14.2.3 Cox
  • Appendix
    • Publishing to BookDown
    • Shiny Apps
    • Packages
      • Create a package
      • 14.2.4 Document Functions with roxygen
      • Create Data
      • Create Vignette
  • References
  • Published with bookdown

My Data Science Notes

11.2 t-SNE

https://www.datacamp.com/community/tutorials/introduction-t-sne