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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Complete R Tutorial To Build Probabilistic Graphical Models!

V
Vidhi Last Updated : 09 Oct, 2020
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction:

Probabilistic Graphical Models (PGM) capture the complex relationships between random variables to build an innate structure. This structure consists of nodes and edges, where nodes represent the set of attributes specific to the business case we are solving, and the edges signify the statistical association between them.

But, why can’t we use the conventional ML models, what is so different about PGM? Constructing the network structure of the underlying model is what makes PGM stand apart from the conventional Machine Learning models.

 

What can we do with this structure?

Ok, so we are emphasizing a lot on this structure, but what is so special about it? Well, the structure carries the belief (embedded in the conditional probability distribution (CPD) tables) about how different variables are related to each other.

We can leverage the independence properties to compactly represent the high dimensional data model via an intuitive diagram, which further helps to carry out the inferences and generate business insights.

As we will primarily build Bayesian Network in this article, it is important to get familiar with the 2 types of Networks:

Bayesian: It is one of the most classical graphical models. Also called as Directed Acyclic Graphs (DAGs), where the direction of the edges represent the direct influence of one variable over another. The flow of the probabilistic influence between the variables in such networks is signaled from the direction of the edge.

Markov: It is an undirected graph where the presence of the edge only represents the association between the two variables, but not the cause and effect relationship.

 

A 3-step process to create and learn the network

 

Source: Author

 

Let’s understand each of the steps briefly before starting to implement them in python:

1) Structure Learning:

We will use a score-based structure learning algorithm known as Hill-Climbing optimization to construct the network structure. The process typically involves starting with an initial structure.

Now, over the space of all possible candidate network structures, we can perform three possible operations i.e. add, delete or reverse the edge.

Starting from an initial structure, keep performing the three operations in the direction that leads to an increase in score. The algorithm starts to converge and hence stops the search of a better structure when the score does not improve further.

Bayesian Information Criterion is the commonly used score.

The source is mentioned in references

2) Parameter Learning:

It is the process of learning the distributions of the network from the underlying data model. The conditional distribution of each node needs to be specified given its parents to build the full Bayesian network. The parameter estimation techniques are adopted to estimate the unknown parameters needed to compute the conditional distributions.

We are using the Maximum Likelihood Estimation method to estimate the parameters of the probability distribution such that the observed data is most probable. And, the point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate.

 

3) Inference: 

 

Source: Author

 

Applications of Graphical models:

1) Fault Diagnosis: Microsoft built a troubleshooting wizard with the intent to cut down the support staff costs and giving users the flexibility to debug on their own.

Tutorial on Bayesian Networks, Troubleshooting Wizard

2) Medical Diagnosis: Bayesian network for medical diagnosis of lung disease

http://probmods.org/chapters/dependence.html

Probabilistic Graphical Models (PGM) capture the complex relationships between random variables to build an innate structure.  finds application in information extraction, speech recognition, computer vision, and many more.

Now that we have covered the key aspects of the graphical models, we will quickly check its uniqueness quotient by experimenting with a dataset.

We will work on the Heart Disease dataset with medical conditions sourced from here.

The data consists of 4300 records, 15 features, and looks like below:

Probabilistic Graphical Models

We need to pre-process the data in the form of missing value imputation and factorizing the variables.

We train the network using cross-validation with the objective to minimize the loss and fitted the model. The Bayesian network constructed from the historical data looks like below:

Probabilistic Graphical Models

 

The trained model object is then queried to generate business insights, some of the examples are shown below:

Inference 1: The probability of the diabetic person having Chronic Heart Disease (CHD) is 33% as compared to 15% for a person with no diabetic history.

 

Probabilistic Graphical Models

 

Inference 2: The probability of the person having ‘Prevalent Hypertension’ increases with age.

Probabilistic Graphical Models

Inference 3: The distribution of the ‘Heart Rate’ changes as follows with the number of cigarettes a person consumes per day.

Probabilistic Graphical Models

Hope you enjoyed reading the article. There is much more that can be done with the power of Graphical models. The complete R code is placed here.

Happy Reading!!!

 

References: 

https://en.wikipedia.org/wiki/Maximum_likelihood_estimation

https://ru.coursera.org/lecture/probabilistic-graphical-models-3-learning/learning-general-graphs-heuristic-search-68xbI

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Vidhi

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Ankit
Ankit

Awesome article Maanvi! Very informative

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Aman
Aman

Very interesting, good job and thanks for sharing such a good article.

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