#OSEMN

[cromacampusinstitute]

OSEMN is an acronym that represents the key stages in the data science process: Obtain, Scrub, Explore, Model, and iNterpret. Each stage plays a crucial role in the overall workflow of a data science project. The process begins with obtaining relevant data from various sources, followed by scrubbing or cleaning the data to address missing values, outliers, and inconsistencies.

[durhamcoolblogmaster]

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[itsmesaisatish-blog]

Still Confused about Confusion Matrix?

As a Data Scientists we do OSEMN Things!

How can we measure the efficiency of our models? Better the efficiency, better the performance and that’s exactly what we want. And it is where the Confusion matrix comes very handy for Model evaluation.

After reading this post you will be clear with concepts like what is confusion matrix ? , key performance metrics to measure the classification models accuracy and how to write python code to create confusion matrix .

 Classification Accuracy: Measuring quality of fit

What is Confusion Matrix?

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix. It’s a performance measurement technique for ML classification. It is a kind of table which helps you to the know the performance of the classification model on a set of test data for that the true values are known.

It’s a table with 4 different combinations of predicted and actual values.

It extremely helpful in assessing the key performance metrics like (Accuracy, Precision, Recall/Sensitivity, Specificity...etc)  

 Let’s make the Confusion Matrix Less Confusing using simple analogy :-) 

Model Evaluation:   Receiver Operating Characteristic Curve

 · ROC curves are commonly used techniques to measure the quality of prediction algorithm.

·  Plot of TPR (Sensitivity) vs FPR (1- Specificity)

 Creating a Confusion Matrix in Python   ·        

Create a simple data set with the predicted values and actual values.

To create the Confusion Matrix using pandas, you’ll need to apply the pd.crosstab as follows:

Displaying the Confusion Matrix using seaborn

To get additional stats using pandas_ml can be used. It can be installed using below command

         !pip install pandas_ml

[reylocrazyfangirl]

Čistá krv (tomione) - Osemnásta kapitola (on Wattpad) https://www.wattpad.com/1227109514-%C4%8Dist%C3%A1-krv-tomione-osemn%C3%A1sta-kapitola?utm_source=web&utm_medium=tumblr&utm_content=share_reading&wp_uname=MeropeMerzmer&wp_originator=kmxdmAO%2BdXFU2qxGR%2BRx0madQXDzp0VbmfgMgD9M4YQ070y6ukv6O7M3UzGbF8vWzRuqbCLhmJgqprBRd0dUW4lfIqjJ7nX8zsC4QA%2BzaytGGhEPpUCOaYLMfVwB1pis Mladý Tom Riddle veril, že sa stane najmocnejším čarodejníkom. A všetky významné čarodejnícke rodiny budú uznávať jeho autoritu. To bolo jeho jediným cieľom. Nikto sa mu neodvážil vzdorovať. Jediná osoba, ktorá sa mu postavila na odpor bola ona. Nové dievča, zaradené do tretieho ročníka. Bezvýznamná a predsa sa mu zdalo, akoby v jej prítomnosti pociťoval len neistotu a zmätok...

[craigbrownphd]

If you did not already know

Progressively Growing Generative Autoencoder (PIONEER,Pioneer Network) We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achieves high-quality reconstruction with $128{\times}128$ images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder-generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks. … Computational Productive Laziness (CPL) In artificial intelligence (AI) mediated workforce management systems (e.g., crowdsourcing), long-term success depends on workers accomplishing tasks productively and resting well. This dual objective can be summarized by the concept of productive laziness. Existing scheduling approaches mostly focus on efficiency but overlook worker wellbeing through proper rest. In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper. It intelligently recommends personalized work-rest schedules based on local data concerning a worker’s capabilities and situational factors to incorporate opportunistic resting and achieve superlinear collective productivity without the need for explicit coordination messages. Extensive experiments based on a real-world dataset of over 5,000 workers demonstrate that CPL enables workers to spend 70% of the effort to complete 90% of the tasks on average, providing more ethically aligned scheduling than existing approaches. … Graph Variogram Irregularly sampling a spatially stationary random field does not yield a graph stationary signal in general. Based on this observation, we build a definition of graph stationarity based on intrinsic stationarity, a less restrictive definition of classical stationarity. We introduce the concept of graph variogram, a novel tool for measuring spatial intrinsic stationarity at local and global scales for irregularly sampled signals by selecting subgraphs of local neighborhoods. Graph variograms are extensions of variograms used for signals defined on continuous Euclidean space. Our experiments with intrinsically stationary signals sampled on a graph, demonstrate that graph variograms yield estimates with small bias of true theoretical models, while being robust to sampling variation of the space. … OSEMN Process (OSEMN) We’ve variously heard it said that data science requires some command-line fu for data procurement and preprocessing, or that one needs to know some machine learning or stats, or that one should know how to `look at data’. All of these are partially true, so we thought it would be useful to propose one possible taxonomy – we call it the Snice* taxonomy – of what a data scientist does, in roughly chronological order: · Obtain · Scrub · Explore · Model · iNterpret (or, if you like, OSEMN, which rhymes with possum). Using the OSEMN Process to Work Through a Data Problem … http://bit.ly/2Zl4t2T

[fbreschi]

Wine is OSEMN

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[cromacampusinstitute]

What is OSEMN in Data Science?

We live in a digital world where vast amounts of data are used for analyses. These data analyses are used to obtain information from those data analysis procedures. Data Science is a field where knowledge is extracted by analyzing and processing vast data using different techniques. Today, Data Science is extensively used in businesses, internet searches, image and speech recognition, AR and VR,…

[cromacampusinstitute]

OSEMN is an acronym that stands for "Obtain, Scrub, Explore, Model, and Interpret." It represents the key stages in the data science workflow. First, data is obtained, often from various sources. Then, it's scrubbed or cleaned to ensure accuracy.

[cromacampusinstitute]

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