Dealing with Class Imbalance —  Dummy Classifiers

Let me paint a picture for you, you are a beginner to the field of Data Science and have started making your first ML model for predictions and found the accuracy using model.score() as 95%. You are jumping around thinking that you nailed it and maybe it was your destiny to become a Data Scientist. Well, I don’t want to burst the bubble but you can be horribly wrong. Do you know why? — Because accuracy is a very poor metric to measure the classifier performance especially in the case of Unbalanced Dataset. And unbalanced datasets are prevalent in a multitude of fields and sectors. From fraudulent transactions, identifying rare diseases, electrical pilferage to classifying search-relevant items in an e-commerce site, data scientists come across them in many contexts. The challenge appears when we have to make a machine learning model that can classify the very rare cases in the training dataset. Due to the disproportionality of classes in the variables, the conventional ML algorithm which doesn’t take into account the class disproportion or balances tends to classify into the class with more instances, the major class, while at the same time gives us a false notion of an extremely accurate model. Both the inability to predict rare events and the misleading accuracy can ruin the whole motive we are making the predictive models for.

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Let me give you an example, suppose you develop a classifier for predicting fraudulent transactions. And after you’ve finished the development, you measure its accuracy on the test set to be 97%. At first, it might seem to be too good to be true, right?

Now let’s compare it to a dummy classifier that always just predicts the most likely class which would be the non-fraudulent transactions. That is regardless of what the actual instance is, the dummy classifier will always predict that a transaction is non-fraudulent. So let’s assume we have testing data which contains 1,000 transaction details, and on average, about 999 of them will be non-fraudulent transactions. So our dummy classifier will correctly predict the non-fraudulent label for all of those 999 transactions. And so the accuracy of the dummy classifier will be 99.9%. So our own classifier’s performance isn’t great at all, as we thought and celebrated. It’s no better than just always guessing the majority class without even looking at the data.

Still not convinced huh! Then lets elaborate it by making a classifier with a real dataset. We shall be using the digits dataset, which contains the images of handwritten digits labeled from 0–9 (i.e. Ten classes).

First and foremost we shall import the necessary libraries and then the load_digits dataset. Now to check whether our dataset is balanced or not, we use the numpy’s bin count method to count the number of instances in each class.

#data #imbalanced-data #artificial-intelligence #classification #data-science #data analysisa

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Dealing with Class Imbalance —  Dummy Classifiers
Lawrence  Lesch

Lawrence Lesch


Superdom: Better and Simpler ES6 DOM Manipulation


You have dom. It has all the DOM virtually within it. Use that power:

// Fetch all the page links
let links = dom.a.href;

// Links open in a new tab = '_blank';

Only for modern browsers

Getting started

Simply use the CDN via

<script src=""></script>

Or use npm or bower:

npm|bower install superdom --save


It always returns an array with the matched elements. Get all the elements that match the selector:

// Simple element selector into an array
let allLinks = dom.a;

// Loop straight on the selection
dom.a.forEach(link => { ... });

// Combined selector
let importantLinks = dom['a.important'];

There are also some predetermined elements, such as id, class and attr:

// Select HTML Elements by id:
let main =;

// by class:
let buttons = dom.class.button;

// or by attribute:
let targeted =;
let targeted = dom.attr['target="_blank"'];


Use it as a function or a tagged template literal to generate DOM fragments:

// Not a typo; tagged template literals
let link = dom`<a href="">Google</a>`;

// It is the same as
let link = dom('<a href="">Google</a>');

Delete elements

Delete a piece of the DOM

// Delete all of the elements with the class .google
delete;   // Is this an ad-block rule?


You can easily manipulate attributes right from the dom node. There are some aliases that share the syntax of the attributes such as html and text (aliases for innerHTML and textContent). There are others that travel through the dom such as parent (alias for parentNode) and children. Finally, class behaves differently as explained below.

Get attributes

The fetching will always return an array with the element for each of the matched nodes (or undefined if not there):

// Retrieve all the urls from the page
let urls = dom.a.href;     // #attr-list
  // ['', '', ...]

// Get an array of the h2 contents (alias of innerHTML)
let h2s = dom.h2.html;     // #attr-alias
  // ['Level 2 header', 'Another level 2 header', ...]

// Get whether any of the attributes has the value "_blank"
let hasBlank =;    // #attr-value
  // true/false

You also use these:

  • html (alias of innerHTML): retrieve a list of the htmls
  • text (alias of textContent): retrieve a list of the htmls
  • parent (alias of parentNode): travel up one level
  • children: travel down one level

Set attributes

// Set target="_blank" to all links = '_blank';     // #attr-set
dom.class.tableofcontents.html = `
  <ul class="tableofcontents">
    ${ => `
        <a href="#${}">

Remove an attribute

To delete an attribute use the delete keyword:

// Remove all urls from the page
delete dom.a.href;

// Remove all ids


It provides an easy way to manipulate the classes.

Get classes

To retrieve whether a particular class is present or not:

// Get an array with true/false for a single class
let isTest = dom.a.class.test;     // #class-one

For a general method to retrieve all classes you can do:

// Get a list of the classes of each matched element
let arrays = dom.a.class;     // #class-arrays
  // [['important'], ['button', 'cta'], ...]

// If you want a plain list with all of the classes:
let flatten = dom.a.class._flat;     // #class-flat
  // ['important', 'button', 'cta', ...]

// And if you just want an string with space-separated classes:
let text = dom.a.class._text;     // #class-text
  // 'important button cta ...'

Add a class

// Add the class 'test' (different ways)
dom.a.class.test = true;    // #class-make-true
dom.a.class = 'test';       // #class-push

Remove a class

// Remove the class 'test'
dom.a.class.test = false;    // #class-make-false


Did we say it returns a simple array?

dom.a.forEach(link => link.innerHTML = 'I am a link');

But what an interesting array it is; indeed we are also proxy'ing it so you can manipulate its sub-elements straight from the selector:

// Replace all of the link's html with 'I am a link'
dom.a.html = 'I am a link';

Of course we might want to manipulate them dynamically depending on the current value. Just pass it a function:

// Append ' ^_^' to all of the links in the page
dom.a.html = html => html + ' ^_^';

// Same as this:
dom.a.forEach(link => link.innerHTML = link.innerHTML + ' ^_^');

Note: this won't work dom.a.html += ' ^_^'; for more than 1 match (for reasons)

Or get into genetics to manipulate the attributes: = '_blank';

// Only to external sites:
let isOwnPage = el => /^https?\:\/\/mypage\.com/.test(el.getAttribute('href')); = (prev, i, element) => isOwnPage(element) ? '' : '_blank';


You can also handle and trigger events:

// Handle click events for all <a> = e => ...;

// Trigger click event for all <a>;


We are using Jest as a Grunt task for testing. Install Jest and run in the terminal:

grunt watch

Download Details:

Author: franciscop
Source Code: 
License: MIT license

#javascript #es6 #dom 

Yashi Tyagi


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