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Reading list

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

Understand Machine Learning Easily Using Python Shapash Library

Akshay
Akshay Last Updated : 28 Apr, 2021
7 min read
This article was published as a part of the Data Science Blogathon.

Topics to be covered

  1. What is Shapash library
  2. The objective of Shapash library
  3. Features of Shapash library
  4. How does Shapash Work
  5. Installation
  6. Getting Started
  7. Conclusion
shapash Library

What is Shapash library?

Model interpretability and intelligibility have been the point of convergence of many research papers and open source commitments. Be that as it may, a large portion of these is equipped with data experts and trained professionals. Shapash is a Python library to imagines AI models’ dynamic interaction. It expects to make AI models reliable for everybody by making them more straightforward and straightforward. Shapash makes straightforward visualizations of global and local reasonableness.

It additionally works with making a web application that can give a great deal of significant worth to end clients and entrepreneurs. Shapash is viable with most sklearn, lightgbm, xgboost, catboost models and can be utilized for classification and regression tasks. It utilizes a Shap backend to figure the local commitment of features, however, this can be supplanted with some other strategy for computing local commitments. Data scientists can utilize the Shapash explainer for investigating and troubleshooting their models or deploy to furnish visualizations with each surmising.

What is shapash Library

Objective of Shapash

1. To show clear and reasonable outcomes: Plots and outputs utilize labels for each component and its modalities:

shapash Library objectives

 

2. To permit Data Scientists to rapidly comprehend their models by utilizing a web app to effortlessly explore among worldwide and neighborhood logic, and see how the various highlights contribute:

shapash Library utilize web app

 

3. To Summarize and export the nearby explanation: Shapash proposes a short and clear nearby explanation. It permits every client, whatever their Data background is, to comprehend a nearby expectation of a managed model, because of a summed up and clear clarification of Shapash Features.

shapash Library summarize

 

4. Full Data science Report

Check the report example here

 

Shapash Features

Some of the features of Shapash are shown below:

  1. Machine Learning models: It works with both classification (Binary or Multiclass problems) and Regression problems. It supports many models like Catboost, Xgboost, LightGBM, Sklearn Ensemble, Linear models, and SVM.
  2. Feature Encoding: It supports a large number of encoding techniques to handle categorical features in our dataset like One Hot Encoding, Ordinal Encoding, Base N Encoding, Target Encoding, or Binary Encoding, etc.
  3. SklearnColumnTransformer: OneHotEncoder, OrdinalEncoder, StandardScaler, QuantileTransformer or PowerTransformer
  4. Visualizations: Provides a set of visuals to easily interpret your results. Display understandable and clear results.
  5. It is compatible with Lime and Shap. It uses Shap backend to show results in just a few lines of code.
  6. It provides a lot of options for parameters to get your results concisely.
  7. Shapash is not difficult to install and utilize: It gives a SmartExplainer class to comprehend your model and sum up clarification with straightforward syntax.
  8. Deployment: Important for investigation and deployment(through an API or in Batch mode) for operational use. Easy web app creation to navigate from global to local.
  9. High versatility: Very couple of contentions are needed to show results. However, the more you work on cleaning and archiving the data, the more clear the outcomes will be for the end client.
features

 

How does Shapash Work

Shapash is a package that makes machine learning understandable and interpretable. Data Enthusiasts can understand their models easily and at the same time can share them. Shapash uses Lime and Shap as a backend to show results in just a few lines of code. Shapash depends on the various advances important to build a Machine Learning model to make the outcomes reasonable. The below image shows the working of the shapash package:

How does Shapash Work

 

It works on the following principle:

  1. First, it compiles the elements of each step like Data Preparation, Feature Engineering, Model Fitting, model Evaluation and Model Understanding
  2. Secondly, It provides a WebApp and charts to understand the model better. We can share and discuss our results with clients.
  3. Lastly, It provides you with a summary of explainability.

Installation

Shapash can be installed using the below code:

pip install shapash

For Jupyter notebook: If you are using jupyter notebook and you want to see inline graphs, then you need to use one more command other than the above command:

pip install ipywidgets

Getting Started

In this section, we are going to explore Shapash by using the dataset House Prices Prediction. It is a regression problem where we have to predict house prices. The link for the dataset is here. First, let’s analyse the dataset including univariate and bivariate analysis then model explainability using Feature Importance, Feature Contribution, Local and Compare plots after that Model performance and finally WebApp.

Dataset Analysis

Univariate Analysis

Use can see the below image for a feature named First Floor Square Feet. We can see a table that shows broad statistics of our train and test dataset like mean, max, min, standard deviation, median, and many more. On the right-hand side, we can see distribution graphs for both training and test datasets. Shapash also mentions whether our feature is categorical or numerical and it also gives you the option of the dropdown. In the drop-down, all the features are available.

Univariate Analysis

 

For categorical features, distinct values and missing values are shown for the training and test dataset. On the right-hand side, a bar plot is shown with the respective percentage of category in a feature.

category in a feature

 

Target Analysis

We can also see the detailed analysis of our target variable called Sales Price. On the left-hand side, all the statistics are shown like count, mean, standard deviation, min, max, median, and many more for both training and prediction datasets. On the right-hand side, a distribution is shown for both training and prediction datasets.

Target Analysis

 

Multivariate Analysis

In the previous section, we had a detailed discussion on univariate analysis. In this section, we are going to see Multivariate Analysis. The below image shows the Correlation Matrix of the top 20 features for both training and testing datasets. A correlation scale is also shown based on different colors. That’s how we can visualize the relation between features using Shapash.

Multivariate Analysis

 

Model Explainability

Feature Importance Plot

By using this package, we can see the feature’s importance. Feature importance is a way to find the importance of input features in predicting the output value. The below image shows the feature importance curve

Feature Importance Plot

 

Feature Contribution Plot

These curves help us to answer the questions like how does a feature impacts my prediction, does it contribute positively or negatively, etc. This plot finishes the significance of the highlights for the interpretability, the global coherence of the model to all the more likely comprehend the impact of a feature on a model.

We can see the contribution plots for both numerical and categorical features.

For numerical feature:

 

Feature Contribution Plot

 

For categorical feature:

 

For categorical feature:

 

Local Plot

We can draw Local plots. The Below image shows the local plot:

Local Plot

 

Compare Plot

We can draw Compare plots. The below image shows the compare plot:

Compare Plot

 

Model Performance

After Data Analysis, we are training the Machine learning model. The below image shows the output of our prediction. On the left-hand side, statistics like count, min, max, median, standard deviation etc are shown for true values and predicted values. On the right-hand side, a distribution is shown for both predicted and actual values.

Model Performance

Web App

After a model training, we can build a WebApp as well. This web app shows a complete dashboard of our data including the things which we have covered till now. The below image shows the dashboard.

Web App

 

For more information, check this Link

 

Conclusion

In this blog, we have studied Shapash in a detailed discussion. You can try this library to automate machine learning tasks and save your time.

You can check my articles here: Articles

Thanks for reading this article and for your patience. Do let me in the comment section. Share this article, it will give me the motivation to write more blogs for the data science community.

Email id: gakshay1210@gmail.com

Follow me on LinkedIn: LinkedIn

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion. 

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Akshay
Akshay

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Responses From Readers

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Mona
Mona

Hiya, thanks for the article, would you please let me know what method plots the correlation Matrix figure in multivariate analysis? Cheers

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