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My guiding vision for the book was simple. One does not need to go through years of culinary schooling in order to prepare a great meal. All you need is a fantastic recipe. I have tried to pack a lot of practical usefulness in little recipes that you can execute on a Sunday afternoon. Take a look through the menu below, and choose your adventure!


Table of Contents for Volume 2: Practice

Note #1: You will notice that most of the table of contents is not yet hyperlinked. This is because I am still working on those posts! I am adding new content every week, please check back soon.

Note #2: Articles that include downloadable content are indicated with a little graphic. For example,  indicates that the article includes a downloadable excel file, or a SQL file , or an R script , etc.

2.1. Your Predictive Modeling Environment
> 2.1.1 The Machine
>> 2.1.1.1 Computer specifications
> 2.1.2 The Software
> 2.1.3 Organizing your workspace
>> 2.1.3.1 Folder organization
2.2. Data Preparation & Exploration
> 2.2.1 Missing Data
2.3. Finding the right predictive variables
> 2.3.1 Linear Least Median Squares Regression
> 2.3.2 Discriminant Analysis
> 2.3.3 Linear Discriminants
> 2.3.4 Quadratic Discriminants
> 2.3.5 Logistic Discriminants
2.4. Making a numeric prediction
> 2.4.1 Regression Analysis
>> 2.4.1.1 Linear Least Mean Squares Regression
>> 2.4.1.2 Linear Least Median Squares Regression
>> 2.4.1.3 Robust Regression
>> 2.4.1.4 Logistic Regression
>> 2.4.1.5 Probabilistic Regression
>> 2.4.1.6 Generalized Linear Model (GLM)
>> 2.4.1.7 Generalized Additive Model (GAM)
>> 2.4.1.8 Multivariate Adaptive Regression Splines
>> 2.4.1.9 PACE Regression
>> 2.4.1.10 Isotonic Regression
>> 2.4.1.11 Project Pursuit Regression
>> 2.4.1.12 Gaussian Process Regression
> 2.4.2 Neural Networks
An algorithm that can be trained using data to identify past patterns and apply these to future data.
>> 2.4.2.1 Inspired from Our Brain
>> 2.4.2.2 Types of Neural Networks
>> 2.4.2.3 The Multi-Layer Perceptron
>> 2.4.2.4 Voted Perceptron
>> 2.4.2.5 Radial Basis Functions
>> 2.4.2.6 Vector Quantization
> 2.4.3 Stochastic Machines
>> 2.4.3.1 Support Vector Machine
>>> 2.4.3.1.1 Sequential Minimal Optimization
>> 2.4.3.2 Boltzmann Machine
>> 2.4.3.3 Simulated Annealing
>> 2.4.3.4 Genetic Algorithms
>> 2.4.3.5 Matrix Factorization Method
> 2.4.4 Time-Series Methods
>> 2.4.4.1 ARMA
>> 2.4.4.2 ARIMA
2.5. Making a categorical prediction
> 2.5.1 Lazy Classifiers
>> 2.5.1.1 Nearest Neighbor
>>> 2.5.1.1.1 k-Nearest Neighbor
>> 2.5.1.2 K* Algorithm
>> 2.5.1.3 Bayesian Rules Classifier
>> 2.5.1.4 Locally Weighted Learning
> 2.5.2 Kernel Methods
>> 2.5.2.1 Kernel Density Estimation
> 2.5.3 Classification Tree Algorithms
>> 2.5.3.1 Rule Tree
>> 2.5.3.2 Naive Bayes Tree
>> 2.5.3.3 CART
>> 2.5.3.4 CHAID
>> 2.5.3.5 Decision Stump
>> 2.5.3.6 Random Tree
>> 2.5.3.7 Random Forest
>> 2.5.3.8 C4.5 or J4.8
>> 2.5.3.9 ID3
>> 2.5.3.10 M5P
>> 2.5.3.11 Alternating Decision Tree
>> 2.5.3.12 QUEST
>> 2.5.3.13 CRUISE
>> 2.5.3.14 GUIDE
>> 2.5.3.15 LOTUS
> 2.5.4 Bayesian Classifiers
>> 2.5.4.1 Averaged, One-Dependence Estimators
>> 2.5.4.2 BayesNet
>> 2.5.4.3 Complement Naive Bayes
>> 2.5.4.4 Naive Bayes
>> 2.5.4.5 Naive Bayes Multinomial
>> 2.5.4.6 Naive Bayes Multinomial Updateable
>> 2.5.4.7 Hidden Naive Bayes
>> 2.5.4.8 DBNBText
>> 2.5.4.9 AODEsr (Subsumption Resolution)
>> 2.5.4.10 WAODE
> 2.5.5 Rule Based Algorithms
>> 2.5.5.1 Decision Table
>> 2.5.5.2 OneR
>> 2.5.5.3 ZeroR
>> 2.5.5.4 Conjunctive Rule
>> 2.5.5.5 PART
>> 2.5.5.6 NNGE
>> 2.5.5.7 PRISM
>> 2.5.5.8 M5Rules
>> 2.5.5.9 RIDOR
>> 2.5.5.10 JRIP
>> 2.5.5.11 Ordinal Learning Method
>> 2.5.5.12 Fuzzy Unordered Rule Induction
2.6. Unsupervised Learning
> 2.6.1 Lazy Classifiers
>> 2.6.1.1 Nearest Neighbor
>>> 2.6.1.1.1 k-Nearest Neighbor
>> 2.6.1.2 K* Algorithm
>> 2.6.1.3 Bayesian Rules Classifier
>> 2.6.1.4 Locally Weighted Learning
2.7. Exploring Complexity
> 2.7.1 Complexity Science
>> 2.7.1.1 Cellular Automata
>> 2.7.1.2 Complex Adaptive Systems
2.8. Measuring Performance
> 2.8.1 Error Types
> 2.8.2 Loss Functions
> 2.8.3 Performance Metrics
>> 2.8.3.1 Metric Selection
> 2.8.4 Validation
>> 2.8.4.1 Split Sampling
>> 2.8.4.2 Cross-Validation
>> 2.8.4.3 Bootstrapping
> 2.8.5 Estimation Error Measurement
>> 2.8.5.1 R-Square
>> 2.8.5.2 Weighted R-Square
>> 2.8.5.3 Adjusted R-Square
>> 2.8.5.4 Absolute Error
>> 2.8.5.5 Prediction Error
>> 2.8.5.6 RMSE
>> 2.8.5.7 Correlation Coefficient
> 2.8.6 Classification Error Measurement
>> 2.8.6.1 Confusion Matrix
>> 2.8.6.2 Sensitivity & Specificity
>> 2.8.6.3 Precision & Accuracy
>> 2.8.6.4 Entropy
>> 2.8.6.5 Kappa Statistic
> 2.8.7 Visualizing Performance
>> 2.8.7.1 Lift Charts
>> 2.8.7.2 ROC Curves
>> 2.8.7.3 Lorenz Curves & Gini Coefficient
2.9. Putting it all together
> 2.9.1 Ensemble Models
> 2.9.2 Crazy, Great Model!
2.10. Relative Performance of Algorithms


Recent posts under: Practice

Since most of the table of contents is not yet hyperlinked, you can see some of the more recent posts below for easier access.

2019
12
Jan
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Your Anaconda install includes Python. Please follow this link in order to install Anaconda & run a test ordinary linear least-squares regression using Python. Link: Install Anaconda & run OLS using Python   Go back to Volume 2: Practice.
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In this post you will: Install Anaconda [~10 minutes] Getting Started with Anaconda Navigator [~3 minutes] Running Ordinary Linear Least Squares (OLS) Regression with Python [~3 minutes] Anaconda is one of the fastest ways to install Python, R, Jupyter etc. It is essentially a software manager that will install and[...]
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In this post you will: Install Julia [will take approx. 10 minutes] Instantiate the Julia kernel from within a Jupyter notebook (using Anaconda Navigator + Jupyter Lab) [~3 minutes] Run Ordinary Least Squares Regression [~5 minutes] Prerequisites: The following posts may be good to review before this one: Installing Anaconda Julia[...]
2018
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