InterpretML: Analysis of SVM and XGBoost models

InterpretML: Analysis of SVM and XGBoost models

Regression modelling using Microsoft’s MimicExplainer. InterpretML by Microsoft is designed with the aim of expanding interpretability of machine learning models.

InterpretML by Microsoft is designed with the aim of expanding *interpretability *of machine learning models. In other words, make those models easier to understand and ultimately facilitate human interpretation.

Microsoft’s Interpret-Community is an extension of this repository, which includes additional interpretability techniques.

In particular, one useful feature is what is called the *MimicExplainer. *This is a type of global surrogate model that allows for interpretability of any black box model.

Background

In this example, the MimicExplainer is used in interpreting regression models built using SVM (support vector machines) and XGBRegressor (XGBoost for regression problems).

Specifically, these two models are used as follows:

  1. SVM is used for predicting the *average daily rate *of a customer using specified features, such as their country of origin, market segment, among others. Original findings are available here.
  2. XGBRegressor is used as a time series regression model to predict the number of weekly cancellations by regressing a lagged series against the actual, i.e. 5 lagged series with sequential lags of up to t-5 are used as features in the model to predict the cancellation value at time tOriginal findings are available here.

The original data is available from Antonio, Almeida and Nunes (2019): Hotel booking demand datasets.

For the purposes of demonstrating how MimicExplainer works, the original models and results are illustrated — with further information on how MimicExplainer can make such results more interpretable.

data-science machine-learning regression time-series-analysis

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Concept of Regression Analysis for Time Series Data

Before start my main topic, I would like to introduce you about Regression Analysis and Time Series Data in shortly. What is Regression Analysis?

15 Machine Learning and Data Science Project Ideas with Datasets

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Finding correlations in time series data

EDA ( Exploratory Data Analysis) is still one of the most important parts of every Data Science/Machine Learning project.

Why Does Stationarity Matter in Time Series Analysis?

Learn the Fundamental Rule of Time Series Analysis: Stationarity is an important concept in the field of time series analysis with tremendous influence on how the data is perceived and predicted.

Preparing data for time series analysis

TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin.