Day 62 - Measures for Accuracy - Classification

This is a video series on learning data science in 100 days. In this video, I have covered the measure that can be used to measure the accuracy of a classification algorithm. I have also included details of their formula and their implementation using python. The measure converted are,

• Confusion Matrix
• Precision
• Recall
• Accuracy Score
• F1 Score

Link to Code:
https://github.com/rsharankumar/Learn_Data_Science_in_100Days

#data-science #python

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How to Speed Up Your Day-To-Day Work in Python

Use all the computing power available to you

In my day-to-day data work, I routinely find myself running a lot of for loops. These can take minutes to complete, which isn’t necessarily a long time, but looping is embarrassingly parallelizable. We can do better.

In this article, I will discuss how to make more efficient use of your time when working in Python. Whether you work on a laptop or a high-performance computer (HPC), you can speed up your workflow by taking full advantage of all the computing power available to you. This can be achieved with the Dask and Dask-jobqueue libraries. This post will discuss how to create and use a dask cluster on your local computer and an HPC.

Dask is a Python library for parallel computing and dask-jobqueue lets you interact with job schedulers, such as Slurm, from a Jupyter Notebook. Dask makes simple things are easier and complex things are possible and itsnumpy and pandas-like API makes writing code familiar to Pythonic data practitioners.

Table of contents

• Installation
• Setup Dask cluster on a laptop
• Setup Dask cluster on an HPC
• Submit work to the cluster
• Dask LabExtension
• Final thoughts

#software-development #python #programming #how to speed up your day-to-day work in python #speed up your day-to-day work #speed up

How Does The Data Size Impact Model Accuracy?

In machine learning, while building predictive models we often come to a situation where we have fewer data. What to do in such scenarios? Do we need a very strong predictive model or more data to build our model? It is often said more data will always result in good performance of a model. But is it correct?

Through this article, we will experiment with a classification model by having datasets of different sizes. We will build a model with less no of data samples and then more no of data samples and then check their accuracy scores. For this, we are going to use the Wine Dataset that is available on Kaggle.

What we will learn from this?

• How the size of the data impacts the accuracy of a classification model?
• Comparison of model accuracy with less and more number of data samples

#developers corner #classification #classification accuracy #logistic regression #machine learning #parameter tuning

Day 62 - Measures for Accuracy - Classification

This is a video series on learning data science in 100 days. In this video, I have covered the measure that can be used to measure the accuracy of a classification algorithm. I have also included details of their formula and their implementation using python. The measure converted are,

• Confusion Matrix
• Precision
• Recall
• Accuracy Score
• F1 Score

Link to Code:
https://github.com/rsharankumar/Learn_Data_Science_in_100Days

#data-science #python

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Still using Accuracy as a Classification Metric?

Accuracy is the most common evaluation metric for classification models because of its simplicity and interpretation. But when you have a multiclass classification problem in hand, say, for example, with 15 different target classes, looking at the standard accuracy of the model might be misleading. This is where “top N” accuracies might be of some use, and in this post, I’ll take you through the basic intuition and python implementation of top N accuracies.
Before we get into top N accuracy, a small refresher on standard accuracy metric:

#python #evaluation-metric #accuracy #machine-learning #classification-algorithms