8 Proven Ways for improving the “Accuracy” of a Machine Learning Model

Enhancing a model performance can be challenging at times. I’m sure, a lot of you would agree with me if you’ve found yourself stuck in a similar situation. You try all the strategies and algorithms that you’ve learnt. Yet, you fail at improving the accuracy of your model. You feel helpless and stuck. And, this is where 90% of the data scientists give up. But, this is where the real story begins! This is what differentiates an average data scientist from a master data scientist. Do you also dream of becoming a master data scientist ? If yes, you need these 8 proven ways to re-structure your model approach. A predictive model can be built in many ways. There is no ‘must-follow’ rule. But, if you follow my ways (shared below), you’d surely achieve high accuracy in your models (given that the data provided is sufficient to make predictions). I’ve learnt these methods with experience. I’ve always preferred to learn practically than digging theories. And, my approach has always encouraged me. In this article, I’ve shared the 8 proven ways using which you can create a robust machine learning model. I hope my knowledge can help people in achieving great heights in their careers.

World Map Panel Plots with ggplot2 2.0 & ggalt

Here’s all you need to use the built-in facet options of ggplot2 to make the 4-panel plot

R is Not So Hard! A Tutorial, Part 21: Pearson and Spearman Correlation

Let’s use R to explore bivariate relationships among variables.

The Value of Accuracy in Predictive Analytics

This article was first posted in 2014 but the message bears repeating. There is a lot being written about tools simple enough for the citizen data scientist to operate. The unstated constraint is that if you don’t have significant experience in data science then these will always be ‘good enough’ models. The problem is that ‘good enough’ models under achieve both revenue and profit. Very small increases in model fitness can translate into much larger increases in campaign ROI. Business sponsors may say they want a quick answer. It’s up to data scientists to show them that more effort on accuracy more than pays off.

Create A D3.js Visualization With Data from AWS S3

D3.js is a popular JavaScript library for manipulating documents based on data. A common pattern for working with d3 is to read in data from .csv, .tsv, or .json files. However, what happens when those data files are large? Storing them in a git repository alongside the code can be inefficient with the repo becoming bloated, and every push taking a long time.

Introducing d3-scale

I’d like D3 to become the standard library of data visualization: not just a tool you use directly to visualize data by writing code, but also a suite of tools that underpin more powerful software. To this end, D3 espouses abstractions that are useful for any visualization application and rejects the tyranny of charts.

ggplot2 version 2 adds extensibility and other improvements

Despite the ggplot2 project — the most popular data visualization package for R — being in maintenance mode, RStudio’s Hadley Wickham has given the R community a surprise gift with a version 2.0.0 update for ggplot2. According to Hadley this is a ‘huge’ update with more than 100 fixes and improvements.