InterpretML: Analysis of SVM and XGBoost 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

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InterpretML: Analysis of SVM and XGBoost models
Ian  Robinson

Ian Robinson

1623856080

Streamline Your Data Analysis With Automated Business Analysis

Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.

Workflow integration and AI capability

Pinpoint unexpected data changes

Understand customer behavior

Enhance marketing and ROI

#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

InterpretML: Analysis of SVM and XGBoost 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

Ray  Patel

Ray Patel

1623292080

Getting started with Time Series using Pandas

An introductory guide on getting started with the Time Series Analysis in Python

Time series analysis is the backbone for many companies since most businesses work by analyzing their past data to predict their future decisions. Analyzing such data can be tricky but Python, as a programming language, can help to deal with such data. Python has both inbuilt tools and external libraries, making the whole analysis process both seamless and easy. Python’s Panda s library is frequently used to import, manage, and analyze datasets in various formats. However, in this article, we’ll use it to analyze stock prices and perform some basic time-series operations.

#data-analysis #time-series-analysis #exploratory-data-analysis #stock-market-analysis #financial-analysis #getting started with time series using pandas

Angela  Dickens

Angela Dickens

1593441000

Analysis, Price Modeling and Prediction: AirBnB Data for Seattle.

Business Understanding

For all AirBnB users and hosts in Seattle, I will analyze and answer business-related questions in these aspects:

  • Price Analysis
  • Listings count Analysis
  • Busiest time Analysis
  • Occupancy rate and Reviews Analysis
  • Modeling for Price Prediction

Questions and answers are covered below.


Data Understanding

Here I will perform Exploratory Data Analysis on the data provided by Inside Airbnb on Kaggle, you can download the data from here(zip file), Zip file contains 3 csv files: listing.csvcalendar.csv, and reviews.csv

Overview of listing.csv

Read the csv file using pandas as given below:

#read listing.csv, and its shape
listing_seattle = pd.read_csv(‘listings_seattle.csv’)
print(‘Shape of listing csv is’,listing_seattle.shape)
listing_seattle.sample(5)    #display 5 rows at random

Basic checks and high-level data analysis

Have a look at the data and have some sanity checks like the percentage of missing values per column, are the listing_ids unique throughout the dataset?, examine the summary of numerical columns, etc.

  • Percentage of missing values in each column

Percentage of missing values per column

From the above bar chart, we get the important columns with the least missing values. Columns like license and square****feet have more than 95% of the data missing, hence we will drop these columns.

Are the ids unique for each row?
len(listing_seattle['id'].unique()) == len(listing_seattle)

Description of all numeric features
listing_seattle.describe()

#data-science #machine-learning #business-analysis #data-visualization #data-analysis #data analysis